Immune checkpoint inhibition therapy is effective in treating a minority of colorectal cancer tumors; however, microsatellite stable tumors do not respond well to this treatment. Emerging cancer immunotherapeutic strategies aim to activate a cytotoxic T cell response against tumor-specific antigens, presented exclusively at the cell surface of cancer cells. These antigens are rare and are most effectively identified with a mass spectrometry–based approach, which allows the direct sampling and sequencing of these peptides. Although the few tumor-specific antigens identified to date are derived from coding regions of the genome, recent findings indicate that a large proportion of tumor-specific antigens originate from allegedly noncoding regions. Here, we employed a novel proteogenomic approach to identify tumor antigens in a collection of colorectal cancer–derived cell lines and biopsy samples consisting of matched tumor and normal adjacent tissue. The generation of personalized cancer databases paired with mass spectrometry analyses permitted the identification of more than 30,000 unique MHC I–associated peptides. We identified 19 tumor-specific antigens in both microsatellite stable and unstable tumors, over two-thirds of which were derived from noncoding regions. Many of these peptides were derived from source genes known to be involved in colorectal cancer progression, suggesting that antigens from these genes could have therapeutic potential in a wide range of tumors. These findings could benefit the development of T cell–based vaccines, in which T cells are primed against these antigens to target and eradicate tumors. Such a vaccine could be used in tandem with existing immune checkpoint inhibition therapies, to bridge the gap in treatment efficacy across subtypes of colorectal cancer with varying prognoses. Data are available   •Transcriptomic profiles highlight differences in colorectal cancer (CRC) subtypes.•Normal adjacent tissues approximate “normal” expression of tumor-specific antigens.•Over 30,000 MHC I–associated peptides identified from CRC cell lines and primary tumors.•Proteogenomic analyses uncover 19 tumor-specific antigens. A novel proteogenomic approach was used to generate personalized cancer-specific databases and to identify more than 30,000 MHC I–associated peptides in colorectal cancer cell lines and primary tumors. Transcriptomic analyses were employed to distinguish microsatellite subtypes of colorectal cancer samples. This led to the first identification of aberrantly expressed tumor-specific antigens in colorectal cancer from both microsatellite stable and unstable tumors, all of which were demonstrably shown to be more abundant in tumors compared with normal adjacent tissues. Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer death worldwide, with over 1.8 million cases and 881,000 deaths estimated in 2018 alone  T cells resulted in tumor regression in one individual but “serious inflammatory colitis” in all patients, demonstrating that an adverse autoimmune response is another possible consequence of targeting TAAs , which are overexpressed in cancer cells compared with normal cells, have been previously identified in CRC , 8. Althing TAAs .The mixed responses to TAA-based therapy suggest that targeting tumor-specific antigens (TSAs) would be more effective. These antigens may be generated through genetic, epigenetic, and posttranslational variations, including but not limited to single nucleotide variants, aberrantly expressed transcripts, or novel splicing events, and are exclusively presented by tumor cells . The higIn the present study, we use an MS-based approach that leverages personalized databases to directly identify TSAs presented by CRC-derived cell lines and tumor biopsies and allows the identification of TSAs from noncoding regions. By using this approach, we identify 19 TSAs across our samples, as well as a variety of TAAs. Furthermore, we identify TSAs in both MSS and MSI tumors, suggesting that MSS tumors present immunologically relevant antigens that could be exploited to bridge the gap in treatment efficacy of ICI in various subtypes of CRC.2.Four CRC cell lines  and one normal fetal small intestine cell line (HIEC6 [ATCC CRL3266]) were obtained from the American Type Culture Collection (ATCC). A description of CRC-derived cell lines is presented in 2. Harvested material was then spun at 1000 rpm for 5 min, rinsed once with warm PBS, then resuspended in ice-cold PBS. After cell count, replicates of 2 × 108 CRC cells were pelleted and frozen at −80 °C until further use. MHC class I surface density of the CRC cell lines was determined by Qifikit (Agilent) using the W6/32 anti-HLA class I antibody (BioXCell), according to the manufacturer’s instructions.For collection, cells were rinsed with warm phosphate-buffered saline (PBS) before being trypsinized with TrypLE Express Enzyme (1×) (Gibco) for 5 to 15 min at 37 °C with 5% COSix pairs of primary human samples consisting of matched colon adenocarcinoma tumor and normal adjacent tissue (NAT) were purchased from Tissue Solutions. Tissue samples were taken from patients receiving surgery as the first line of treatment and were flash frozen in liquid nitrogen. More information about primary tissue samples can be found in For RNA extraction of cell lines, 1 to 2 million cells were collected and washed once with ice-cold PBS. The cells were then resuspended in Trizol (Invitrogen). Total RNA was isolated using the RNeasy Mini kit (Qiagen) or the AllPrep DNA/RNA/miRNA Universal kit (Qiagen) as recommended by the manufacturer, for cell lines and tissues, respectively.Five hundred nanograms of total RNA was used for library preparation. RNA quality control was assessed with the Bioanalyzer RNA 6000 Nano assay on the 2100 Bioanalyzer system (Agilent Technologies), and all samples had an RNA integrity number (RIN) above 6.8 for NAT and above 8 for cancer samples. Libraries were prepared with the KAPA mRNAseq Hyperprep kit (Roche). Ligation was made with Illumina dual-index UMI (IDT). After being validated on a BioAnalyzer DNA1000 chip and quantified by QuBit and qPCR, libraries were pooled to equimolar concentration and sequenced with the Illumina Nextseq500 using the Nextseq High Output 150 (2 × 75 bp) cycles kit. A mean of 129 and 95 million paired-end PF reads were generated for the cell lines and tissue samples, respectively. Library preparation and sequencing were performed at the Genomic Platform of the Institute for Research in Immunology and Cancer (IRIC).Sequences were trimmed using Trimmomatic version 0.35  and alighttps://github.com/FRED-2/OptiType)  . HLA allhttps://github.com/xjtu-omics/msisensor-pro)  .p-value, we selected only genes that were significantly differentially expressed in all six subjects for GO term analysis with ∣log2 fold change∣ >1. When examining DEGs between MSS and MSI tissues, the same fold change thresholds were applied. For GO term analysis of MSI DEGs, genes were selected that were exclusively differentially expressed in both MSI tissues . For GO term analysis of MSS DEGs, genes were considered if they were differentially expressed in three or more MSS tissues.DESeq2 version 1.22.2  was usedThe proportion of various biotypes in the transcriptome of tissue samples was determined as described . Brieflyhttps://pcingola.github.io/SnpEff/#snpeff)  .https://pachterlab.github.io/kallisto) was used to quantify transcript expression in TPM (https://github.com/ekg/freebayes) into PyGeno  and thymic epithelial cells (TECs) as a positive control because they (1) express a large collection of self-peptides and (2) establish central tolerance in the thymus (negative selection of T cells). mTECs (n = 6) and TECs (n = 2) were thus used to generate the cancer-specific proteome for cell lines . The respective NAT for each primary tumor sample was used in place of mTECs for this portion of database construction, as it approximates “normal” expression for that subject. RNA-Seq reads were cut into 33-nucleotide sequences known as k-mers and only k-mers present <2 in mTECs or matched NAT for cell lines and tissues, respectively, were kept. Overlapping k-mers were assembled into contigs, which were then three-frame translated in silico. Of note, short peptide sequences generated through the k-mer approach were then concatenated into longer sequences of approximately 10,000 amino acids. To reduce the number of small separate sequences in the cancer-specific proteome, these peptides were concatenated using the “JJ” sequence as a separator, which is recognized internally by the PeaksX+ software to split sequences upon occurrence of this sequence. Then, the canonical proteome and the cancer-specific proteome were concatenated to create the global cancer databases. Cell line databases consisted of 3.38 × 106 sequences on average.Global cancer databases were constructed as described . In brien in TPM . Sample-o PyGeno . Annotat8 cells per replicate, four replicates per cell line) were resuspended with PBS up to 2 ml and then solubilized by adding 2 ml of ice-cold 2× lysis buffer (1% w/v CHAPS). Tumor and NAT samples (average 568 mg) were cut into small pieces , and 5 ml of ice-cold PBS containing protein inhibitor cocktail  was added. Tissues were first homogenized twice for 20 s using an Ultra Turrax T25 homogenizer (IKA-Labortechnik) set at a speed of 20,000 rpm and then 20 s using an Ultra Turrax T8 homogenizer (IKA-Labortechnik) set at speed 25,000 rpm. Then, 550 μl of ice-cold 10× lysis buffer (5% w/v CHAPS) was added to each sample. After 60-min incubation with tumbling at 4 °C, tissue samples and CRC cell line samples were spun at 10,000g for 30 min at 4 °C. Supernatants were transferred into tubes containing 1 mg of W6/32 antibody covalently cross-linked protein A magnetic beads, and MAPs were immunoprecipitated as described  columns and stored at −20 °C until injection. Labeling efficiency was calculated using PeaksX+ search results (see “MAP extracts were resuspended in 200 mM HEPES buffer pH 8.1. Tandem mass tag (TMT) reagent (Thermo Fisher Scientific), 50 μg, in anhydrous acetonitrile was added to samples as follows: CRC cell line replicates were labeled with TMT6plex (lot #UG287166) channels TMT6-126 to 129; tissue samples were labeled with TMT10plex (lot # UH285228) -126 (NAT) and -127N (tumor). Samples were gently vortexed and reacted at room temperature for 1.5 h. Samples were then quenched with 50% hydroxylamine for 30 min at room temperature, then were diluted with 4% formic acid (FA) in HDried peptide extracts were resuspended in 4% FA and loaded on a homemade C18 analytical column (20 cm x 150 μm i.d. packed with C18 Jupiter Phenomenex) with a 106-min gradient from 0% to 30% ACN (0.2% FA) and a 600-nl/min flow rate on an EASY-nLC II system. Samples were analyzed with an Orbitrap Exploris 480 spectrometer (Thermo Fisher Scientific) in positive ion mode with Nanoflex source at 2.8 kV. Each full MS spectrum, acquired with a 240,000 resolution was followed by 20 MS/MS spectra, where the most abundant multiply charged ions were selected for MS/MS sequencing with a resolution of 30,000, an automatic gain control target of 100%, an injection time of 700 ms, and collisional energy of 40%. The LC-MS instrument was controlled using Xcalibur version 4.4 .Database searches were conducted using the PeaksX+ software, version 10.6 (Bioinformatics Solutions Inc) . Error ti.e., although a source gene may generate more than one unique peptide, it would only be counted once in the source gene analyses). For tissues, only source genes shared by four or more tissues were included in this analysis.GO term analysis was performed for CRC-derived cell lines and primary tissues with the Metascape tool . A list MAP-coding sequences (MCSs) were quantified in RNA-Seq data as described . BrieflyTo investigate what proportion of tissue sample MAPs were derived from certain transcript biotypes, the most abundant putative source transcript based on kmer-per-hundred-million (KPHM) quantification was determined. For peptides from the cancer-specific (kmer) database, the MCSs were reverse translated into all possible nucleotide sequences and all possible genomic regions able to code for a given MAP were identified see “” above. kmin value into several k-mers detected per 108 reads sequenced (kphm) using the following formula: kphm = (kmin × 108)/rtot, with rtot representing the total number of reads sequenced in a given RNA-Seq experiment. Peptides were kept only if their RNA coding sequences were expressed at least 10-fold higher in cancer than in normal  and expressed <2 KPHM in normal. Subsequent filtering removed any peptides with indistinguishable isoleucine/leucine variants; a peptide with an IL variant was kept only if the most expressed variant met the above-mentioned criteria. The MCSs of the remaining peptides were quantified in RNA-Seq data as described above and were kept only if their expression was <8.55 RPHM in mTECs and other normal tissues (GTEx). Genomic localization for each peptide was assigned by mapping reads containing each MCS to the reference genome (GRCh38.99) using BLAT (https://genome.ucsc.edu/cgi-bin/hgBlat). Peptides were excluded if the genomic localization was unclear or if they mapped to a hypervariable region . Finally, the MS/MS spectra of the remaining candidates were manually validated. Peptides were classified as mTSAs if their amino acid sequence was different from the reference and if the mutation was not a known germline polymorphism. Peptides were classified as aeTSAs if they were overexpressed ≥10-fold in tumor compared with normal and ≤0.2 KPHM in mTECs (and NAT in the case of tissues) and as TAAs if they were overexpressed ≥10-fold in cancer but the expression in mTECs and/or NAT was >0.2 KPHM. Ultimately, the transcript of origin of each TSA/TAA was attributed by selecting the most highly expressed peptide-overlapping transcript from the kallisto quantification file  and used to query each TSA candidate coding sequence’s 24-nucleotide-long k-mer set. The number of reads fully overlapping a given peptide-coding sequence was estimated using the k-mer set’s minimum occurrence (kmin), as in general, one k-mer always originates from a single RNA-Seq read. We then transformed this ile see “” sectionTo examine the intertumoral distribution of TSA and TAA sequences in other CRC tumors, the log(RPHM+1) expression of the peptide coding sequences in 151 colon adenocarcinoma (COAD) samples from TCGA was determined see “”.https://github.com/masato-ogishi/Repitope) (The predicted immunogenicity of MAPs of interest was determined with the R package Repitope v3.0.1 (epitope) .https://bitbucket.org/levitsky/pyteomics) and reproducibly detected peptide fragments were identified. Root scaled intensities of these fragments were correlated between endogenous and synthetic peptide scan pairs, and SciPy v1.2.1 (https://www.scipy.org/) was used to compute Pearson correlation coefficient, p-value, and confidence intervals. Mirror plots of the scan pair with the lowest p-value were generated for each peptide using spectrum_utils v0.2.1(https://github.com/bittremieux/spectrum_utils).Synthetic peptides of TSA and select TAA sequences were obtained from Genscript. Synthetic peptides were solubilized in dimethyl sulfoxide to a concentration of 1 nmol/μl, and all synthetic peptides were combined in a stock solution at a concentration of 10 pmol/μl. The stock solution was desalted in aliquots of 150 pmol on homemade C18 membrane (Empore) columns and dried using a Speed-Vac. Dried peptide extracts were labeled with a TMT10plex channel as described see “” sectionm/z, Orbitrap resolution of 120,000, automatic gain control (AGC) of 5.0e5, and a maximum injection time of 50 ms, using an inclusion list for the peptides of interest. We used a 3s top speed approach for MS2 in the ion trap, with an isolation window of 0.4 m/z, collision induced dissociation of 35%, a “normal” ion trap scan rate mode, 2.0e4 AGC target, and 50 ms maximum injection time. This was followed by the selection of eight synchronous precursor ions for MS3 acquisition, which was done with a scan range of 110 to 500 m/z, Orbitrap resolution of 50,000, AGC of 1.0e5, maximum injection time of 300 ms, an isolation window of 2.0 m/z, and 65% HCD collision energy. LC-MS instrument was controlled using Xcalibur version 4.4 . Error tolerances for precursor mass and fragment ions were set to 10.0 ppm and 0.5 Da, respectively. A nonspecific digest mode was used. TMT10plex was set as a fixed posttranslational modification, and variable modifications included phosphorylation (STY), Oxidation (M), Deamidation (NQ), and TMT10plex STY. For quantification, PSMs were filtered to exclude those with contamination in the TMT10plex-127C channel and to select those within the 70th intensity percentile. MS2 precursor profiles and intensity profiles of all relevant channels were manually inspected to select peptides for quantification. Intensity ratios for each peptide were calculated using the average TMT10plex-127N and TMT10plex-126 intensities of good-quality PSMs.To relatively quantify MAPs of interest in primary tissue samples, synthetic peptides at concentrations of 10, 100, or 1000 fmol labeled with TMT 10plex-129N, 130N, and 131N, respectively, were spiked into remaining purified MAPs from NAT and CRC tissue samples labeled with TMT10plex-126 and 127N, respectively. Note that the channel TMT 10plex-127C was left empty to assess contamination. Samples were analyzed with an Orbitrap Fusion Tribrid spectrometer (Thermo Fisher Scientific) in positive ion mode with Nanoflex source at 2.4 kV. For synchronous precursor selection MS3 (SPS-MS3), full MS scans were acquired with a range of 300 to 1000 BioRender.com. The majority of other figures were created with Python v3.7.6, R v3.6.3, or Origin (Pro)2019b. R packages include:https://github.com/masato-ogishi/Repitope) (Repitope v3.0.1 (epitope) ,https://github.com/hms-dbmi/UpSetR) (UpsetR v1.4.0 (/UpSetR) ,https://github.com/rcastelo/GSVA) (GSVA v1.38.2 (lo/GSVA) ,https://bioinformatics.mdanderson.org/estimate/) (ESTIMATE v1.0.13 (timate/) .p-values are determined using two-sample t test, except in the determination of significance for immunogenicity scores, in which case the Mann–Whitney test was used as the data did not have a normal distribution, as determined by the Shapiro test. For t tests, we performed f-tests to determine whether the dataset had significant variation; if yes, then we used the t test assuming variation, and otherwise the t test assuming no variation was used. For CRC-derived cell lines, four technical replicates of 2 × 108 cells were prepared, which were TMT labeled and multiplexed prior to injection. Owing to limited tissue material, half of the purified MAPs from primary samples were injected to obtain global immunopeptidomic data and the remaining sample was used for targeted analysis with synthetic peptides to confirm the sequences and abundance of putative TSAs and select TAAs. To select high-quality PSMs for quantification, those of low intensity or with contamination in an empty TMT channel were excluded. Furthermore, only peptides with favorable MS2 precursor and intensity profiles were quantified.To effectively elucidate the MHC I immunopeptidome of colorectal cancer, four CRC cell lines and six samples from human subjects consisting of both matched tumor and NAT were selected. NAT was used as an approximation of healthy tissue, as it is the most effective control for each respective tumor. Since no matched samples were available for cell lines, a pool of eight TEC samples was used in the creation of global cancer databases, to obtain a wide berth of approximate normal RNA expression. All instances of To determine the composition of the immunopeptidome of CRC, we analyzed a collection of samples comprising four CRC-derived cell lines and six sets of primary adenocarcinoma samples, which consist of matched tumor and NAT (Tables 1 and 2). Paired-end RNA-Seq allowed the creation of a global cancer database, consisting of a canonical cancer proteome as well as a cancer-specific proteome for each sample, by generating cancer-specific kmers, which, once combined into contigs, are translated into three reading frames to encompass noncanonical sequences from any genomic origin . Thus, iWe used immunoprecipitation to isolate MHC I–peptide complexes, and we labeled the eluted MAPs with TMT isobaric labeling reagent, as TMT labeling was recently shown to enhance the detection of MAPs by increasing their charge state and hydrophobicity . We then5 to 5.07 × 105 MHC I molecules/cell, as determined by Qifikit, and a diversity of HLA alleles, which were identified using OptiType, an HLA genotyping tool that uses RNA-Seq data to predict a sample’s HLA alleles, in combination with the HLA alleles for these cell lines documented in the literature ..5 to 5.0All of the primary tumor samples are derived from stage II adenocarcinomas, which vary only slightly in tumor grade and TNM (tumor-node-metastases) classification . The CRCi.e. 15% of sporadic CRC and 90% of nonpolyposis CRC)  , in thissis CRC) . Althougsis CRC) , 49, 50,A) or cell lines (A) confirms their distinct transcriptomic profile. Accordingly, pathway and process enrichment analysis of biopsy samples revealed a transcriptomic profile enriched in terms associated with their tumorigenic status (B). As expected for CRC, the most significantly up- and downregulated terms are, respectively, linked to cell proliferation  and muscle phenotype and contractility . Although the enrichment of terms related to proliferation and cell cycle is a general hallmark of cancer (A and S3A). Although MSI samples tend to cluster tightly together, MSS tumors appear more dispersed and therefore transcriptionally more heterogeneous. Functionally, when analyzed separately, the MSS and MSI CRC samples are enriched in very different gene sets. When compared with their corresponding NAT, MSI tumors are characterized by a significant upregulation of various immune-related terms (B), whereas MSS tumors are more associated with an increased expression of genes related to both Wnt and PI3K-Akt signaling (C). Although the link of these two signaling pathways with CRC is well established in the literature , and with an enrichment score for known tumor-infiltrating leukocyte (TIL) markers (A). Although all NAT samples presented similar levels of immune infiltration, MSI and MSS tumors were, respectively, characterized by increased and decreased immune infiltration scores (C and S4A). Consistent with what has been previously reported in the literature . Although we cannot exclude the possibility that the higher proportion of noncoding RNAs could at least in part reflect the different cellular composition of the tumor and NAT samples, it is well documented that noncoding RNAs are frequently deregulated in cancer (E and S4B). Considering both cell line and tissue samples together resulted in a statistically significant difference in the number of INDEL mutations between MSS and MSI samples (p = 0.0024) (C). Because both MSI and INDEL accumulations result from defects in the DNA mismatch repair pathway  C. Becaus pathway , one can8 cells for each cell line and from each tissue sample. We then derivatized each replicate with a separate TMT6plex channel  for cell lines or with TMT10plex-126 and -127N for primary NAT and tissue samples, respectively. The four replicates of each cell line, and half of the respective NAT and tumor MAPs from each subject, were multiplexed and analyzed by LC-MS/MS. The median labeling efficiencies were 72.4% or 87.8% for cell lines and tissue samples, respectively. We ascribe the lower efficiency of labeling in cell lines to meager MAP yields. We identified 5281 and 27,583 unique MAPs in the cell line and tissue datasets, respectively, with a mean of 1433 unique MAPs per cell line and 5855 per tissue .To elucidate the MHC I immunopeptidomes of CRC-derived cell lines and tissues, we immunoprecipitated MAPs from four replicates of 2 × 10A and B). This large proportion of unique MAPs can be attributed to the diversity of HLA alleles among our samples, which is a major factor influencing which peptides can be presented at the cell surface (C and C]), thus their MHC I immunopeptidomes have approximately 23% similarity, as measured by the Jaccard index. (D). The next closest similarity in MAP repertoires between two tissues is 1328 MAPs shared by the two MSI tissues (S5 and S6), which is only an 11% similarity between their immunopeptidomes. Despite the decreased MAP identification in cell lines, these trends are reproduced. For example, HCT116 and RKO share the most MAPs, although these peptides represent only 4% similarity, and this is likely a feature of their larger peptide repertoires (C). In contrast, COLO205 and SW620 share 152 MAPs, approximately 10% similarity.When taking the cell line and tissue samples together, we identified a total of 30,485 unique MAPs. Within the MAP repertoire of each sample, 32% to 68% of the peptides are sample specific, and very few shared MAPs were observed when comparing only cell line or primary samples , A and B surface C and S2.d index. D. The neertoires C. In conTo contextualize these comparisons, we can again consider the HLA alleles of our samples. Of the 2079 MAPs shared by S1 and S6, 1595 MAPs are predicted to bind the same allele HLA-B∗07:02 in approximately 90% of these cases . In addiA, upper panel). This was highly correlated with the number of MAPs identified . Roughly 6% to 14% of the source genes in a given immunopeptidome were sample specific , which could be attributed to sample-specific biological features or could reflect an imperfect sampling of the immunopeptidome . We do not expect to identify every MAP presented at the cell surface, and as a majority of source genes in each sample are attributable to only a single MAP (C–E). Thus, distinct cell lines appear to be less homogenous than tissue samples at the source gene level. This likely reflects differences in sample composition, as the tissue samples have source genes derived from NAT, stroma, infiltrating cells, etc., whereas cell lines consist of only a single cell type. In addition, lower MHC I presentation of cell lines and the resulting decreased identification of MAPs means fewer source genes were sampled, lowering the likelihood of overlap. Regardless, all samples are more similar at the source gene level compared with the immunopeptidome level, and sample-specific MAPs are being derived from shared source genes.At the gene level, we identified peptides derived from over 8000 unique source genes, with an average of 1014 and 3168 source genes per cell line and tissue sample, respectively . Thus, despite the large diversity of HLA alleles between and among our cell lines and tissue samples and the low MAP identification in cell lines, there is significant similarity in terms of what genes are contributing to the MHC I immunopeptidome.To obtain an overview of the genomic function of the MHC I immunopeptidome and investigate the overlap of source genes, we performed GO term analysis on all the source genes identified in the cell lines as well as those identified in four or more tissues. Several common features between cell lines and tissues are detectable at the immunopeptidome level, including a significant enrichment of genes involved in RNA metabolism, ribonucleoprotein complex biogenesis, translation, and cellular responses to stress D. Thus, i.e. UTR or CDR) . Approximately 4.2% of peptides are from noncoding regions if we include the 2.8% of peptides deriving from unannotated RNA transcripts, as these peptides are likely coming from intergenic sequences. Approximately one-third of all noncoding MAPs (including those from unannotated transcripts) are derived from nonsense-mediated decay transcript products, whereas less than 1% of them are coming from long noncoding RNA (lncRNA), nonstop decay products, retained introns, or processed transcripts (transcripts that do not contain open reading frames) .To investigate what proportion of MAPs from our tissue samples were from noncoding transcripts, we first determined, for each peptide, the most abundant putative source transcript (Ensembl Annotation 99). For peptides from the cancer-specific database, we mapped the MCS onto the genome and determined the most expressed transcript at that location (see “i.e. not an SNP). MAPs for which the sequence was the same as the reference genome and overexpressed at least 10-fold in tumor compared with normal were classified as aberrantly expressed TSAs (aeTSAs) if they had no or residual RNA expression (≤0.2 KPHM) in mTECs (and NAT in the case of tissues) or as TAAs if their expression in mTEC and/or NAT was greater than 0.2 KPHM.Following the identification of over 30,000 unique MAPs, we filtered peptide-coding sequences to select those overexpressed at least 10-fold in cancer and expressed ≤2KPHM in pooled TEC samples or matched NAT, for cell lines and primary samples, respectively. A recent immunopeptidomic study in acute myeloid leukemia demonstrated that MCSs with RPHM <8.55 have less than 5% probability to generate MAPs . We thusA). Overall, we identified 1 putative TSA in a CRC-derived cell line and 18 putative TSAs in primary tissues, and the TSA yield from each sample was correlated with the number of MAPs identified (Pearson’s r = 0.76) (B). Among the TSAs from coding regions, two were from noncanonical reading frames, deriving from exon frameshift sequences, and another two were mutated TSAs identified in MSS tissues S2 and S3 (A and B). Among the noncoding TSAs, a large proportion originated from intronic or intergenic regions, with a smaller number being derived from 5′ UTR, 3′ UTR, or lncRNAs (B). The sequences of six aeTSAs  overlapped endogenous retroviral element (ERE) sequences  . Of thes = 0.76) B. Among 2 and S3 , A and B lncRNAs B. The seequences . Owing tequences , 65. Of equences . The majequences .Fig. 4NoC). In contrast to the primarily noncoding putative TSAs, the majority of the TAAs we identified were from canonical, exon-coding sequences, with only a small number being derived from introns or intergenic sequences (D). Two noncanonical TAAs overlapped ERE sequences . This could be due to S6 having a lower “degree” of instability, as reflected in the MSIsensor-pro results . Eight of these were also reported in the HLA Ligand Atlas, with one of them specifically being documented in healthy colon tissue. Interesting, none of the TAAs previously identified in these earlier publications were reported as tumor antigens, and, conversely, six of the 12 tumor antigens of interest reported in Loffler et al. were also identified in the immunopeptidomes of our work, although they did not pass our TSA or TAA selection criteria, most often due to high expression in NAT . This analysis naturally does not include peptides derived from intergenic regions. Note that, in Figure 5A, both S4 and S5 plots have a canonical TAA point that is not visible, as it overlaps with another canonical TAA source transcript; however, these sequences were still included in downstream analyses. The average log2FC for the source transcripts of our putative TSAs and TAAs in the samples in which they were identified was 3.6 and 3.2, respectively. In some instances, the source transcript of an aeTSA was only slightly more abundant in the tumor than in the NAT; however, this reflects only the overall abundance of the entire transcript, and the peptide-coding sequences were in fact more abundant in the cancer . The TSA sequences were not expressed above 8.55 RPHM in any healthy tissues, except RIGGVGVEK, an aeTSA identified in S2, which was expressed above threshold in the testis. This suggests that this TSA could also be classified as a cancer-testis antigen (CTA), a class of aeTSA that is expressed in male germ cells but may also be aberrantly expressed in cancer. Owing to the absence of MHC I in testis, these antigens are also promising candidates for cancer immunotherapy  B. The TSotherapy . This puotherapy . TAA expversus matched NAT. These candidates all had MS/MS that correlated well with those of the synthetic peptides, with Pearson correlation score ≥0.6 . To evaluate the sharing potential of our antigens, for each peptide of interest, we first calculated the average of log-transformed (log(rphm+1)) values of pooled GTEx (n = 2442) and mTEC (n = 8) samples. Overall, nine TSAs (53%) and nine TAAs (100%) had an expression ≥10-fold above their corresponding averaged GTEx/mTEC value in at least 5% of TCGA COAD tumors. This demonstrates that TAAs are more frequently shared among COAD TCGA tumors than their TSA counterparts. However, this also means that most TSAs are highly shared in these samples.To examine the intertumoral distribution of these TSAs and TAAs in other CRC tumors, we plotted the log(RPHM+1) expression of the peptide coding sequences in 151 colon adenocarcinoma samples from The Cancer Genome Atlas (TCGA) A. To evaB). In addition, aeTSAs had significantly higher immunogenicity scores compared with canonical TAAs and coding TAs overall (TSAs and TAAs derived from coding regions). In fact, TAAs from canonical regions were predicted to be significantly less immunogenic than thymic peptides (p < 0.01). This could be partially due to the low number of TAAs that we validated. If we consider these predictions with the entire set of 31 TAAs, this is no longer the case . Many of the antigens in our samples are prevalent, and an estimation with the IEDB population coverage tool predicted that 80.64% of the US population expresses at least one of the alleles associated with the TAs identified in this study.Finally, we sought an approximation of the proportion of individuals who possess the alleles that are predicted to bind and present our tumor antigens C. Many oMass spectrometry is currently the best method to identify MAPs of interest, as it can directly sample the MHC I immunopeptidome and eliminates the need for error-prone prediction software, which are unable to incorporate the largely misunderstood intricacies of MAP processing and presentation . Our appC and S3B) and more favorable responses to ICI (particularly PD-1 inhibition) compared with their MSS counterparts  and increased immunogenicity of MSI in CRC suggested that these tumors would be characterized by a larger TSA or TAA burden. Although the MSI tissue samples were sources of many TSAs and TAAs, we were able to identify 8 aeTSAs, 2 mTSAs, and 18 unique TAAs in MSS tissues . Thus, it could be that the unfavorable response of MSS tumors to ICI is not due to a lack of tumor antigens but rather to a lack of immune activation against these antigens. Accordingly, when considering all 31 of our identified TAA sequences, we saw a statistically significant decrease in the Repitope immunogenicity scores of TAs derived from MSS tissues compared with their MSI counterparts . mTSAs are immunologically relevant and have the capacity to be immunogenic, but owing to their lack of sharing between individuals, their potential for use in large-scale immunotherapy is limited.Among these TSAs, we identified two mTSAs unique to tumors derived from PLK1 and HDSP1, with missense mutations A520T and V345I occurring in 27% and 49% of RNA-Seq reads, respectively. The HDSP1 mutation is predicted to be benign by software such as Polyphen and CADD , 76. AltFigs 1in vitro studies.Although mTSAs are expected to be rare and unique to a given tumor, it has been shown that aeTSAs can be shared among patients . Here, wA). Of the nine TSAs that are expressed ≥10-fold above their corresponding averaged GTEx/mTEC value in at least 5% of TCGA COAD tumors, three are from intergenic sequences, two from exons, two from exon frameshifts, and one each from intronic or 5′ UTR sequences. This small sample size prevents us from drawing any conclusions; however, there may be a therapeutic advantage to distinguishing highly shared TSAs from those that are less abundant across COAD populations. Although high tumoral RNA expression of a TSA sequence does not guarantee MHC I presentation of that peptide, it does increase the likelihood that a given TA sequence, or perhaps other sequences from the same transcript, could have dysregulated MHC I presentation in cancer.Outside of our six tissue samples, the decreased sharing of some TSAs among TCGA COAD tumors suggests that certain TSA sequences are not widely shared A. Of theA). This is to be expected owing to the very nature of TAAs, which are expressed in normal tissues but overexpressed in cases of malignancy, compared with TSAs, which arise only in cases of mutated or aberrantly expressed sequences. As such, TAAs are more challenging to use in immunotherapy approaches as they have been known to induce autoimmune responses, or even T cell tolerance . However, TAAs can certainly be advantageous as cancer biomarkers, as is the case with CEA, the first TAA discovered in CRC in the mid-1960s  . The incFig. 6olerance . We alsoid-1960s .et al. 2018  and instrument sensitivity, we present here the identification of 19 TSAs. An additional drawback of this study was the low MAP identification in CRC cell lines attributable to the low MHC I abundance at the cell surface, which decreased the probability of identifying TSAs. In the future, MHC I presentation of cell lines could be boosted with IFN-γ treatment to increase identification, particularly of lowly abundant peptides . Future via the PRIDE partner repository (PXD028309 and 10.6019/PXD028309.The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  via the pository  with theGSE195985 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195985).The transcriptomic data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  and are This article contains J. C., M.-P. H., P. T., and C. P. are named inventors on a patent application filed by Université de Montréal and covering antigens described in this article.


The SKACP003-induced growth inhibition induced apoptosis, as evidenced by a decrease in BCL-2 and an increase in BAX and caspase gene  expression. Reduced expression of the β-Catenin signaling pathway was associated with the SKACP003-induced apoptosis. SKACP003-treated breast cancer cells showed decreased expression of Wnt/β-Catenin targeting genes such as C-Myc, P68, and COX-2 and significant downregulation of CDK-4 and CDK-6 genes. Furthermore, cytoplasmic β-catenin protein levels in SKACP003-treated cell lines were significantly lower than in control cell lines. The results of the current study suggest that the newly identified antimicrobial peptide SKACP003 has great potential as a candidate for specifically targeting the β-catenin and thus significantly reducing the progression and prognosis of breast cancer cell lines.Despite progress in breast cancer treatment, the survival rate for patients with metastatic breast cancer remains low due to chemotherapeutic agent resistance and the lack of specificity of the current generation of cancer drugs. Our previous findings indicated that the antimicrobial peptide SKACP003 exhibited anticancer properties, particularly against the MCF-7, MDA-MB-231, and MDA-MB-453 breast cancer cell lines. However, the mechanism of SKACP003-induced cancer cell death is unknown. Here, we investigated the molecular mechanism by which SKACP003 inhibits the cell cycle, cell proliferation, and angiogenesis in breast cancer cell lines. The results revealed that all the breast cancer cell lines treated at their IC The prevalence of breast cancer among women results in a significant reduction in their average lifespan. Similarly, the mortality ratio is skewed in favor of urban women compared to their rural counterparts. Most concerning is that, as per estimates, the number of women impacted by this problem is expected to rise exponentially in the near future . RegardiConsequently, it is imperative to investigate alternative therapies involving an oncolytic agent as a potential targeted therapy against cancer cells. Antimicrobial peptides (AMPs) are a promising option in this regard (a more meaningful option) because AMPs have been shown to provide the necessary immunity against a wide variety of pathogens . More imAnticancer peptides (ACPs) are short antimicrobial peptides (5–40 amino acids) with anticancer properties. ACPs generally have a low molecular weight and a cationic nature at physiologically relevant pH levels. These ACPs have a high degree of precision in their action and can easily penetrate tumor walls. Furthermore, they are easily synthesized in the laboratory in large quantities. With the characteristics mentioned earlier and the ability to interact only with cancerous cells while leaving healthy cells, ACPs can be considered a critical component in cancer treatment ,14. By tAlthough several signaling pathways are available in the literature to explain the progression of breast cancer, the Wnt signaling pathway is the most comprehensive. The Wnt family of glycoproteins determines cell fate during embryonic development and maintains tissue homeostasis in adult organisms . WithoutThis study builds on our previous work , which f50 concentration  after treatment with the peptide SKACP003 at its ICntration A. In addntration B, most nntration . This mo50 concentrations for 24 h, and the corresponding expression of CDK-4 and CDK-6 genes was evaluated. After treatment with SKACP003, the CDK-4 and CDK-6 genes were significantly downregulated in the breast cancer cell lines compared to the control cell lines  and vascular endothelial growth factor (VEGF-A) that promote metastasis and angiogenesis . PreviouTherefore, the current study employed RT-PCR analysis to assess MMP-2, MMP-4, and VEGF-A gene expression in response to SKACP003 and confirmed that treatment significantly decreased the expression of all MMP genes and the VEGF-A gene. The results also showed that the SKACP003 successfully inhibited cell migration in all tested breast cancer cell lines .In general, apoptosis is inhibited or downregulated in almost all cancer cells. So, the efficacy of SKACP003 as a vital anticancer agent is determined in terms of its ability to induce cancer cell death in the treated cell lines. The expression of BCL-2, BAX, Cas-3, Cas-8, and Cas-9 genes is useful for studying apoptosis because these genes are thought to be the most important players in the process. Consequently, the effects of SKACP003 on the expression of the genes mentioned above were examined. As seen in The direct inhibitory effect of SKACP003 on the cytoplasmic β-catenin level has been shown in our previous studies using in silico and experimental evaluations such as the MTT  assay, staining procedures, cell cycle assay, and the expression of the downstream genes that are regulated by β-catenin protein . As a reWnt signaling is vital because it promotes tumor progression by upregulating various critical factors. Furthermore, the deregulation of Wnt signaling is the primary indicator of most cancers, including breast cancer. To date, even a modest amount of knowledge about the Wnt signaling pathway’s function in cancer has contributed to the advancement of effective therapeutic alternatives ,35,36,37Recent studies have shown that AMPs can selectively target and kill cancer cells while leaving healthy cells ,41,42. SAs a follow-up to our previous work on identifying the novel antimicrobial peptide, SKACP003, which targeted the β-catenin and curtailed the Wnt signaling pathway, the present study attempted to understand better the inhibitory action of SKACP003 against β-catenin protein and its downstream target genes.The current study used three human breast cancer cell lines: MCF-7, MDA-MB-231, and MDA-MB-453. Cell lines treated with the peptide analogue showed significant inhibition of DNA synthesis after 24 h, followed by significant inhibition of cell growth after 48 h. Only viable cells were counted during the growth studies . In the Cell cycle analysis reveals implications that necessitate further investigation into the molecular mechanism responsible for cell cycle arrest. Deregulated activation of cell division kinases (CDKs) has been linked to erratic cell division, prompting the present study to examine the expression of CDK-4 and CDK-6 genes. According to the results, SKACP003 was able to suppress the expression of CDK-4 and CDK-6 in breast cancer cell lines . Increas68, and COX-2, suggested that they were significant factors in breast cancer progression [Studies on the expression of oncogenes, such as C-Myc, Pgression ,52. C-Mygression ,54,55,56gression . Based oThrough the action of β-catenin, an additional significant process that must be taken into account regarding the Wnt signaling pathway is the stimulation of the expression of proteins that play a role in the angiogenesis process, such as MMP and VEGF-A . TherefoDeveloping an effective anticancer drug must consider apoptosis, or programmed cell death. Previous research from our research group demonstrated that SKACP003 induced apoptosis in all breast cancer cell lines . HoweverThe Western blot study adds to the evidence that SKACP003 inhibits cancer cells growth by selectively attacking and destroying the cytoplasmic β-catenin protein . OverallAll the cell culture supplies, including Dulbecco’s modified Eagle’s medium (DMEM), 10% fetal bovine serum (FBS), penicillin/streptomycin, phosphate-buffered saline (PBS), and dimethyl sulfoxide (DMSO), were purchased from Hi-Media Laboratories, India. The cDNA Synthesis Kit, TRIzol reagent, and Emerald PCR Master Mix were acquired from Takara, Japan. The β-catenin antibody (SC-376841), produced from mice, and β–actin antibody (SC-8432), were obtained from Santa Cruz Biotechnology, Dallas, TX USA.2 in a CO2 incubator . All experiments were performed using cells from passage 15 or less.The human breast cancer cell lines MCF-7, MDA-MB-231, and MDA-MB-453 were purchased from the National Centre for Cell Science (NCCS), Pune, India. The cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin/streptomycin as antibiotics in a T-25 flask at 37 °C in a humidified atmosphere of 5% COThe chemical structure of the peptide SKACP003 was drawn using ChemSketch software . Using P5 cells and exposed to SKACP003 in culture medium at their respective IC50 concentrations . The control cells were only provided with the growth medium. The cells were harvested at the end of the 24 h incubation at 37 °C, washed with a PBS solution devoid of calcium and magnesium, fixed with 70% ethanol, and then frozen at 4 °C for 24 h. The cells were washed twice with Dulbecco’s phosphate-buffered saline (DPBS) before being re-suspended in 500 μL of propidium iodide (PI) solution containing 1 μL RNase A (10 μg/μL), 1 μL PI (10 μg/μL), and 0.5% Tween-20 in 500 μL of DPBS and incubated for 30 min at room temperature in the dark before analysis. Cells were collected using an argon-ion laser at 488 nm on a FACS Calibur II system , and the cell-cycle distribution was analyzed using the FlowJO software. Typically, 10,000 events were collected for each sample.Flow cytometry is one of the most effective tools for analyzing the mode of cell death induced by a tested drug . Breast Total mRNA was extracted using the TRIzol reagent from MCF-7, MDA-MB-231, and MDA-MB-453 breast cancer cell lines under control and treatment conditions . The cDNA for each RNA was synthesized using a First Strand cDNA Synthesis Kit and reverse transcriptase-mediated polymerase chain reaction (RT-PCR) with 1 µg of RNA as the template . To amplify the cDNA, the Emerald PCR Master Mix  was used, with specific primers  of the gAll control and treated breast cancer cell lines were washed several times with warm PBS before being lysed on ice with 200 µL RIPA Lysis Buffer , proteinase inhibition, and a phosphatase inhibitor cocktail . Following a 20-min incubation, lysates were cleared by centrifugation at 10,000 rpm for 15 min at 4 °C. Supernatants were quantified using Lowry’s method, and absorbance at 655 nm was measured using a microplate reader.Protein samples extracted from both the treated and control cells (50 μg) were added to a 4X sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) along with 20% glycerol, and sample buffer . The samples were boiled for 10 min at 95 °C before being resolved on 12% SDS-PAGE at 100 V for 2 h. Then, protein samples were transferred into nitrocellulose membranes in a transfer buffer containing 39 mmol/L glycine, 20% methanol, and 48 mmol/L Tris for 120 min at 220 mAmp. Following this, the membranes were blocked for 2 h in a solution of 5% skim milk diluted in Tris-buffered saline (TBS) containing 0.1% Tween 20 (TBST). Next, the membranes were incubated overnight at 4 °C with a specific primary antibody . β-catenin and β-actin primary antibodies were used to determine β-catenin and β-actin protein levels. Following incubation with the secondary antibodies  for 2 h at 4 °C, the blots were washed three times for 10 min in TBST. The blots were then washed three times in TBS for 5 min, three times in TBST for 10 min, and developed with BCIP/NBT .p < 0.05 value was considered statistically significant. All the results were expressed as mean value ± standard deviation.A two-way ANOVA with Dunnett’s post-test was performed using Graphpad Prism, version 6.04 , to compare treated cell lines with the controls. A SKACP003 is effective in all measures of cancer elimination activity. Therefore, it may be integral to developing a more potent and side-effect-free drug for breast cancer treatment. Along these lines, additional research, such as clinical trials, might be continued in the future. Furthermore, to achieve the highest possible levels of selectivity, feasibility, and safety, SKACP003 may be combined with chemotherapy or nanotechnology.


Development of novel anti-cancer treatments requires not only a comprehensive knowledge of cancer processes and drug mechanisms of action, but also the ability to accurately predict the response of various cancer cell lines to therapeutics. Numerous computational methods have been developed to address this issue, including algorithms employing supervised machine learning. Nonetheless, high prediction accuracies reported for many of these techniques may result from a significant overlap among training, validation, and testing sets, making existing predictors inapplicable to new data. To address these issues, we developed CancerOmicsNet, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, CancerOmicsNet integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. The performance of CancerOmicsNet, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches. CancerOmicsNet generalizes well to unseen data, i.e., it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. CancerOmicsNet is freely available to the academic community at https://github.com/pulimeng/CancerOmicsNet. Cancer is perhaps best understood as a complex system of interacting molecular-level networks, such as nuclear and cell networks, influenced by local and distant factors . The nucOne of the earliest graph information processing techniques is a graph neural network (GNN) that employs a graph structure to learn the representation of the input data . The majCompared to other types of biological networks, gene co-expression networks have certain advantages, such as a high coverage of human genes, the additional knowledge obtained from the biomedical literature, and the possibility to study different cancer subtypes. , 18. OneAnother group of methods utilize dual-layer cell line-drug networks, constructed by integrating drug similarity and cell line similarity networks in a weighted fashion, to predict the drug sensitivity of cancer cells. These techniques build on the observation that chemically similar drugs exhibit similar inhibitory effects on different cell lines and vice versa, similar cell lines tend to respond comparably to a treatment with the same drug. Dual-layer models typically require the optimization of various parameters, such as weights for individual drugs and cell lines, in order to determine the relative contribution of each network to the final prediction. As an example, a dual-layer network was developed to evaluate separately the response of a known cell line to a new drug and the effect of a known drug against a new cell line using a linear weighted model, followed by combining these two quantities into a sensitivity score for the treatment of a particular cell line with a drug . Encoura50 values. Further, a similarity-regularized matrix factorization method, or SRMF, predicts anticancer drug responses of cell lines solely from the chemical structures of drugs and the baseline gene expression levels in cell lines [More advanced methods combine genomics with drug chemical and activity information to predict the response to drugs in cancer treatment. For instance, the Cancer Drug Response Profile scan, or CDRscan, predicts anticancer drug responsiveness based on the drug screening assay data, the genomic profiles of human cancer cell lines, and the molecular fingerprints of drugs . The anall lines . Those tNotwithstanding these encouraging reports, there are two drawbacks of currently available techniques to predict the response of cancer to drug treatment. First, most of these methods employ hand-crafted features simply exploiting similarities between instances, i.e., they essentially look for similar combinations of cell lines and drugs with known therapeutic outcomes. In reality, similar cell line-drug combinations may not necessarily produce the anticipated effects. Explicit similarity-based approaches are also unlikely to reveal the underlying mechanisms of the response of cancer to drug treatment. Second, the performance of many existing algorithms is likely grossly overestimated due to randomly splitting the redundant data into training, validation, and testing subsets resulting in a significant overlap among these sets. To address both issues, we developed CancerOmicsNet, a GNN-based algorithm employing multiple graph convolutional blocks with the attention-based propagation and a sophisticated graph readout mechanism to predict the effect of a drug treatment on the cancer cell growth. This novel method utilizes compact, cancer-specific networks constructed from protein-protein interactions, differential gene expression, disease-gene association, and drug inhibition data. The generalizability of CancerOmicsNet is carefully evaluated in a series of cross-validation benchmarks against different tumor tissues.Input for CancerOmicsNet are cancer-specific networks assembled from multiple heterogeneous data including protein-protein interactions (PPIs), differential gene expression (DGE), disease-gene association (DGA) scores, kinase inhibitor profiling (KIP), and growth rate inhibition (GR). The procedure of data integration is schematically presented in After the initial network is constructed , protein50 value of 0.8 nM in a cell-free assay [50 ranging from 21.7 to 138 nM [50 values for dasatinib (6.8 and 8.8). Finally, the entire graph is assigned a label describing the effectiveness of the drug therapy against a given cell line. Since the growth of MDA-MB-468 cell line is inhibited by 30% 48 hrs after the treatment with dasatinib at 3 μm [max value [Subsequently, the inhibition data against dasatinib are added to the network. Dasatinib inhibits SRC with an ICee assay  and diffo 138 nM . Two kin at 3 μm  and the ax value  is –0.96Cancer-specific networks are subsequently subjected to a reduction procedure devised to produce graphs that are more compact yet richer in the biological information. This algorithm is presented in st order neighbors is passed between nodes in a single block, using multiple sequential blocks propagates the information from higher order neighbors.CancerOmicsNet implements a GNN model to predict the response of cancer cell lines to a treatment with kinase inhibitors. The GNN employs graph convolutions, which are functionally equivalent to matrix convolutions in the convolutional neural network (CNN) working with images. Similar to the CNN propagating the information of a pixel to its neighbor pixels, the GNN propagates the information of a node in the graph to its neighbor nodes. The architecture of CancerOmicsNet is presented in st order neighbor, node 2  for 49,348 small molecules and 411 kinases were collected from Team-SKI [50 (the negative logarithm of IC50) of 6.3, which is equivalent to 500 nM in terms of IC50. The DGA data were obtained from the DISEASE database [Input graphs are constructed by mapping multiple heterogeneous data, DGE, KIP, DGA, and GR, on the human PPI network. STRING v11 database  has beenTeam-SKI  and filtdatabase  of 8,330database  of 24,1650 and GRmax, quantify the proliferation with the value of growth rate inhibition (GR) based on time course and endpoint assays [50 is the concentration of a drug at which GR is 0.5, whereas GRmax is the maximum measured GR value. Negative GRmax values correspond to the cytotoxic response and positive values correspond to the cytostatic response. In this study, we employ six LINCS-Dose-Response datasets, Broad-HMS LINCS Joint Project, LINCS MCF10A Common Project, HMS LINCS Seeding Density Project, MEP-HMS LINCS Joint Project, Genentech Cell Line Screening Initiative, and Cancer Therapeutics Response Portal [50 values set to infinity or multiple GR50 values for a particular cell line-drug combination, the final dataset comprises 359 cell lines, 29 drugs, and 3,549 cell line-drug combinations. The number of positive instances (the cytotoxic effects of drugs on cell lines) is 2,124, whereas the number of negative instances (cytostatic responses) is 1,425.Recent drug response metrics, GRt assays . GR50 ise Portal . The oriA procedure devised to reduce the size of drug-cell line networks employs the topological information and the biological knowledge. Two neighboring nodes are merged when the following conditions are met, both nodes are kinase proteins, share the same gene expression, and belong to the same GOGO  cluster The most widely adopted propagation protocol transmit the information equally without considering the importance of a node to its neighbors and to the graph. This protocol can be expressed as t is the propagation step, D is the degree matrix of the adjacency matrix A, and X(t) represents embeddings at the propagation step t. Note that the original node features can be denoted as the 0-th propagation step, X(0). It is obvious that not all nodes have the same importance to their neighbors. For instance, many non-kinases in our dataset contain no useful information because these proteins are normally expressed, have no association with a disease, and are not targets for inhibitors. The information propagated from such proteins should be less important compared to the information transmitted from kinases and other proteins differentially expressed and having high disease associations. On that account, we added a propagation attention mechanism to increase the importance of these nodes. Specifically, we implemented a mechanism to learn a dynamic and adaptive summary of the local neighborhood, which operates only in the feature space [i to node i,jj, γ, is defined as where re space . The attN (i) denotes the neighbors of node i and β is a trainable parameter. Essentially, the attention is the softmax of feature cosine similarities between center nodes and their neighbors. By utilizing the attention mechanism, the original propagation matrix calculated from the degree and adjacency matrices shown in where where each entry of the propagation matrix Γ is calculated using After the information is propagated, the embeddings of each node need to be updated. Many techniques are available to generate node embeddings, and each has its advantages and disadvantages. Based on a series of preliminary experiments, we decided to implement a model inspired by the graph isomorphism network (GIN) . The GINwhere Θ denotes a neural network, Γ is the propagation matrix calculated using CancerOmicsNet employs JK-Net followed by a Set2Set model to generate a global representation of the input graph from the node-wise information. JK-Net exploits varying influence radii of different layers to learn the best representation of the entire graph. This model can integrate outputs from individual graph convolutional blocks with three strategies, the concatenation, the max-pooling, and the LSTM attention. Considering the size of our data, we decided to employ the max-pooling strategy since it does not introduce any additional hyperparameters. This particular strategy performs a feature-wise max-pooling with lower layers favoring the local information and higher layers mostly containing the global graph information. With the max-pooling scheme, JK-Net automatically selects the most informative neighborhood size for each feature coordinate. Once the information from different layers is aggregated, we adopted the Set2Set model  as a finCancerOmicsNet is compared to several other methods to predict the growth rate of cancer cell lines after drug treatment against the same dataset and employing the same cross-validation protocol. The graph isomorphism network (GIN) incorporates the graph isomorphism test to generate node embeddings preserving the original graph structure at each propagation step . As a reCancer Drug Response Profile scan (CDRscan) is a deep learning model predicting drug response from cancer genomic signature . CDRscan


KRAS mutational statuses. The two KRAS inhibitors demonstrated different anti-tumor efficacy and displayed synergistic or additive effects, when combined with downstream pathway inhibitors. These data emphasized the importance of KRAS as a therapeutic target for PDAC and indicate two distinct mechanisms of KRAS inhibition and their interactions with downstream pathway inhibitors.Small molecule inhibitors and targeted therapy are considered to have significant potential for pancreatic ductal adenocarcinoma therapies. Preclinical studies of novel inhibitors and inhibitor combinations can elucidate their acting mechanisms and provide valuable data for in vivo research and clinical trials. We explored the antitumor efficacy of KRAS inhibitors BI-3406 and sotorasib alone or in combination with the downstream inhibitors trametinib and buparlisib in PDAC cell lines, characterized by different KRAS) mutations are widespread in pancreatic ductal adenocarcinoma (PDAC) and contribute significantly to tumor initiation, progression, tumor relapse/resistance, and prognosis of patients. Although inhibitors against KRAS mutations have been developed, this therapeutic approach is not routinely used in PDAC patients. We investigated the anti-tumor efficacy of two KRAS inhibitors BI-3406 (KRAS::SOS1 inhibitor) and sotorasib (KRAS G12C inhibitor) alone or in combination with MEK1/2 inhibitor trametinib and/or PI3K inhibitor buparlisib in seven PDAC cell lines. Whole transcriptomic analysis of combined inhibition and control groups were comparatively analyzed to explore the corresponding mechanisms of inhibitor combination. Both KRAS inhibitors and corresponding combinations exhibited cytotoxicity against specific PDAC cell lines. BI-3406 enhance the efficacy of trametinib and buparlisib in BXPC-3, ASPC-1 and MIA PACA-2, but not in CAPAN-1, while sotorasib enhances the efficacy of trametinib and buparlisib only in MIA PACA-2. The whole transcriptomic analysis demonstrates that the two triple-inhibitor combinations exert antitumor effects by affecting related cell functions, such as affecting the immune system, cell adhesion, cell migration, and cytokine binding. As well as directly involved in RAF/MEK/ERK pathway and PI3K/AKT pathway affect cell survival. Our current study confirmed inhibition of KRAS and its downstream pathways as a potential novel therapy for PDAC and provides fundamental data for in vivo evaluations.Kirsten rat sarcoma virus ( KRAS) is one of the most frequently mutated oncogenes in human pancreatic ductal adenocarcinoma (PDAC); oncogenic KRAS mutations can be detected in approximately 92% of the PDAC genomes [KRAS gene encodes the protein KRAS, which is a guanosine triphosphatase (GTPase), and regulates signal transduction by cycling between active guanosine triphosphate (GTP) bound and inactive guanosine diphosphate (GDP) bound statuses [KRAS point mutations downregulate the GTPase activity of RAS and prevent the GTPase from promoting the conversion of GTP to GDP. The status of permanent GTP-binding activates downstream signaling pathways, such as the PI3K/AKT pathway or RAF/MEK/ERK pathway, which in turn leads to the initiation and development of PDAC [KRAS cooperates with other common oncogenes, such as TP53, CDKN2A, BRCA3, SMAD4, etc., to cause the initiation and development of PDAC [Kirsten rat sarcoma viral oncogene homolog  shortened the median overall survival of PDAC patients [ surgery . At the reatment . Anotherpatients .KRAS in PDAC, a growing number of studies consider KRAS as a target for the treatment of PDAC. Sotorasib is the first small molecule inhibitor against KRAS G12C mutations and was approved by the FDA for the treatment of non-small cell lung cancer (NSCLC) in 2021. Studies have reported that it can effectively inhibit various cell lines that carry KRAS G12C mutations, including PDAC cell lines [Due to the important role of ll lines . Accordill lines . Currentll lines . HoweverKRAS G12C mutations accounted for only 1.42% of all KRAS mutated PDAC patients. The specific inhibitors for KRAS G12D and G12V mutations, which currently represent the majority , are still under development and have not yet entered any clinical trials [KRAS mutations is also an urgent problem to be solved. It is well known that there are dynamic positive feedback and negative feedback regulation loops in the RAS signaling pathway. A key role in this feedback regulation is the guanine exchange factor son of sevenless 1 (SOS1) [KRAS mutations [KRAS mutant tumor cell lines in vivo and in vitro, including KRAS G12C/V/S/A, and G13D, and also achieved excellent efficacy in the PDAC cell line MIA PACA-2. Moreover, the experimental animals displayed good tolerance to BI-3406 treatment [Although the KRAS G12C inhibitors achieved satisfactory effects on its corresponding mutation, l trials . Therefo1 (SOS1) . In unst1 (SOS1) ,24. Moreutations . Based oreatment . TherefoKRAS have achieved encouraging results, there are still limitations that exist, especially for PDAC. At present, most studies still focus on NSCLS, while little attention has been paid to PDAC. There are also few studies that investigate the combined application of multiple inhibitors. In the existing studies on PDAC, only the MIA PACA-2 cell line was investigated. As a result, we were unable to evaluate the efficacy of these KRAS inhibitors on PDAC cells carrying other KRAS mutations. Therefore, we studied the efficacy of multiple KRAS mutation inhibitor BI-3406 and specific KRAS mutation inhibitor sotorasib in different KRAS mutations and wild-type KRAS PDAC cell lines. At the same time, we explored the efficacy of KRAS inhibitors and their downstream pathways (PI3K/AKT/mTOR pathway and RAF/MEK/ERK pathway) inhibitors in combination. RNA sequencing was performed after the combined application to explore the mechanism of the influence of the multi-inhibitor combination on the pathway.Although studies on the inhibition of BI-3406 (KRAS::SOS1 inhibitor) was purchased from Chemietek , sotorasib (KRAS G12C inhibitor), buparlisib (pan-PI3K inhibitor), and trametinib (MEK1/2 inhibitor) were purchased from Selleck Chemicals . According to the manufacturer’s instructions, all inhibitors were separately dissolved in dimethyl sulfoxide (DMSO)  as a stock solution, at a final concentration of 10 mM. The stock solutions were stored at −80 °C and diluted into corresponding working concentrations before each experiment.2 incubator with a humidified atmosphere at 37 °C.PDAC cell lines ASPC-1, BXPC-3, CAPAN-1, COLO357, PATU8902, and T3M4 were kindly provided by the University Medicine Greifswald and MIA PACA-2 was kindly provided by Prof. Robert Jaster from Rostock University Medical Center. ASPC-1, BXPC-3, COLO357, and T3M4 were cultured in RPMI1640 medium , supplemented with 10% heat-inactivated fetal calf serum (FCS) (PAN-Biotech) and 1% penicillin-streptomycin solution (P/S)  (PAN-Biotech). CAPAN-1 was cultured in RPMI1640 medium, supplemented with 15% heat-inactivated FCS and 1% P/S solution. MIA PACA-2 was cultured in DMEM medium (PAN-Biotech), supplemented with 1% heated-inactivated FCS and 1% P/S solution. PATU8902 was cultured in DMEM/F12 medium (PAN-Biotech), supplemented with 10% heated-inactivated FCS and 1% P/S solution. After verifying that all cell lines were not contaminated by mycoplasma, these PDAC cell lines were maintained in a 5% CO4 cells per milliliter in a 24-well plate  or a 96-well plate . After 24 h, the supernatant was discarded and media containing increasing concentrations  of inhibitors or vehicle  were added to the corresponding PDAC cell lines.For the single inhibitor application experiments, the PDAC cell lines were seeded at a density of 3.3 × 10The results of single inhibitor application and related experiments were comprehensively analyzed, and specific PDAC cell lines and inhibitor concentrations were selected for further combined application experiments and the concentrations are listed in 2. At the indicated time points, all cell experiments evaluated at least three biologically independent replicates.The treated cells were incubated for 72 h at 37 °C with 5% COProliferation was evaluated by absolute counting, which was determined by trypan blue  staining. After inhibitor exposure in 24-well plates, the cells were harvested and washed with 1× PBS (PAN-Biotech). Following the cells being stained with trypan blue, the number of viable cells was determined by counting with a hemocytometer. Proliferation was expressed as a percentage of viable cells treated with the inhibitor to the vehicle-treated control (control = 100%).®-Multi Microplate Multimode Reader. The absorbance value of the reference wavelength was subtracted from that of the corresponding measuring wavelength. The value of cells exposed to the vehicle was used as a control and the value of culture media was used as the background. The background value was subtracted from the control and experimental values. The amount of CV directly correlates to cell biomass. The result is expressed as a percentage of the inhibitor-treated group to vehicle-treated controls (control = 100%).Biomass quantification was carried out by crystal violet (CV) staining. After exposure to the corresponding inhibitors, cells in 96-well plates were washed once with PBS and stained with 50 μL 0.2% CV solution on a shaker at room temperature for 10 min. Thereafter, the plates were washed twice with PBS. To elute bound CV, 100 μL 1% sodium dodecyl sulfate (SDS) was added to each well and incubated on a shaker at room temperature for 10 min. Finally, absorbances at a measuring wavelength of 570 nm and at the reference wavelength of 620 nm were measured by a Promega GloMax−/PI− cells were considered to be viable cells, Annexin V+/PI− cells were considered to be early apoptotic cells, and Annexin V+/PI+ cells were considered to be late apoptotic/necrotic cells. Flow cytometry measurement was performed on FACSVerse™  and all data were analyzed by BD FlowJo™ software .Apoptosis and necrosis were evaluated by Annexin V FITC  and propidium iodide (PI) (Sigma-Aldrich Chemie GmbH) double staining by flow cytometry. After exposure to the vehicle control, and both single and combined inhibitors, cells were harvested and washed twice with cold PBS. After the washing step, the cell pellet was resuspended in 100 µL Annexin V binding buffer (1×) , and incubated with 5 µL of Annexin V FITC for 15 min at room temperature in the dark. Then, cells were stained with PI  straightway before measurement. Unstained and single-stained cells were used to determine the negative and positive boundaries and measured in each experiment. Annexin VOE) and predicted (PE) inhibition of the combination therapy.The interaction among the inhibitors was evaluated by the Bliss independent model. The interaction of the inhibitor combination was determined by the difference between the observed , and two cell slides were made for each group. After 24 h of air-drying, the slides were stained with May–Grünwald solution  for 6 min, washed with phosphate buffer solution (pH = 7.2) (Merck) three times for 1 min, then stained with Giemsa solution (1:10) (Merck) for 20 min, and washed with phosphate buffer solution three times for 1 min again. After the slides were air-dried for 24 h, the morphology of cells was examined and visualized with Evos XL Core Imaging System , magnified 100 times. Each experiment was repeated 3 times to eliminate random errors.Examination of PDAC cell line morphology changes was carried out by Pappenheim staining. After 72 h of exposure to the vehicle control, single inhibitor, or combined inhibitor, supernatants were collected and cells were harvested. After counting the cells, we resuspended the cell pellet and adjusted the cell density of the control group and each experimental group to 5 × 106 cells were harvested and washed twice with cold sterile PBS. Cell pellets were resuspended in 700 μL QIAzol Lysis Reagent (QIAGEN GmbH), then the aqueous phase that contains the total RNA of the lysed cells was extracted and purified by a silica membrane of RNeasy Mini spin columns. At last, total RNA was eluted by 30 μL of RNAse-free water.Total RNAs were extracted using the miRNeasy Mini Kit  according to the manufacturer’s instructions. For each cell line, only the RNA of the DMSO control group and the triple inhibitor application group were extracted. In brief, at least 5 × 10After extraction, RNA concentrations, as well as OD 260/280 and OD 260/230 ratios, were measured with the NanoDrop 1000 Spectrophotometer .The RNA quality was assessed using the Agilent RNA 6000 Nano Kit  on the 2100 Bioanalyzer system (Agilent Technologies Inc.). Only samples with an RNA integrity number (RIN) >8 were proceeded to DNA library preparation using the Illumina Stranded mRNA Sample Preparation Kit . Briefly, 800 ng of total RNA was enriched for mRNA via poly-T oligo-coated magnetic beads, and chemically fragmented under elevated temperature. The RNA fragments were then reverse-transcribed into the first- and second-strand cDNA using random hexamers. Double-stranded cDNA fragments were ligated with anchor primers and PCR-amplified for 10 rounds, using 10bp unique dual index primers (UDIs). The quality of the libraries was evaluated for fragment length distribution on the Agilent DNA-1000 Chip (Agilent Technologies Inc.). The library concentration was quantified using a Qubit dsDNA HS Assay kit (Life Technologies), normalized to 2 nM and equally pooled. The multiplexing library pool was sequenced for 2 × 101 bp paired-end reads at a final loading concentration of 750 pM on the NextSeq 2000 system and P3 Flow Cell at the sequencing facility of Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.2(Fold Change)| > 1 and adjusted p value (padj) < 0.05 were considered analytically valuable and proceeded to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.Sample de-multiplexing and FASTQ generation of raw sequencing reads were conducted using on-board DRAGEN BCL Convert analysis workflow (Illumina). The data were quality-checked pre- and post-processing using FastQC version 0.11.9 . Data prp-value < 0.05 and q-value < 0.25 were considered to have a significant impact and were selected for further analysis.The GO and KEGG enrichment analysis were applied for the functional annotation and pathway analysis using the gene set enrichment analysis (GSEA) ,34. The p < 0.033, **: p < 0.002, ***: p < 0.001 versus the control group.Each experiment was performed in at least 3 biologically independent repetitions. Results of proliferation, biomass quantification, and apoptosis/necrosis analysis were expressed as mean ± standard deviation (SD). Statistical significance was determined by one-way ANOVA (after proving the data within each group conformed to the Gaussian distribution) or Kruskal–Wallis test (the data within each group conformed to the non-Gaussian distribution) and displayed as follows: *: KRAS mutational statuses: one KRAS wild-type cell line (BXPC-3), one KRAS G12C (c.34G>T) cell line (MIA PACA-2), one KRAS Q61H (c.183A>C) cell line (T3M4), two KRAS G12D (c.35G>A) cell lines (ASPC-1and COLO357), and two KRAS G12V (c.35G>T) cell lines (CAPAN-1 and PATU8902). The information about each cell line includes the chromosomal location (#Chr), the zygosity , reference base (Ref), observed base (Obs), allele frequency (VAF), base change, and amino acid substitution, which are listed in KRAS genotype.The analyzed seven PDAC cell lines were characterized by the following KRAS G12C inhibitor sotorasib had almost no inhibitory effect on the KRAS Q61H cell line T3M4  and COLO357 (VAF: 23.8), which both carry KRAS G12D, are quite different; cell proliferation decreased by 50% and 37%, and biomass decreased by 41% and 27%, respectively. Sotorasib appears to be more effective against KRAS G12D mutations with high VAF.The ine T3M4 . At the KRAS G12C mutation. A significant inhibitory effect can be observed at a concentration of 0.005 μM, while at 0.05 μM, cell proliferation and biomass were reduced by 69% and 60%, respectively . At the highest tested concentration, cell proliferation and biomass were only reduced between 30 and 50% . At the highest test concentration of 10 μM, cell proliferation only decreased by 11% and 17%, and the biomass decreased by 12% and 21%, respectively . In addi and 50% .For BI-3406 in combination with trametinib and buparlisib, a significant increase in the inhibition of cell proliferation and biomass was observed when compared with the DMSO control group, regardless of whether double-inhibitor combinations or triple-inhibitor combinations were tested . When coFor the combination of sotorasib with trametinib and buparlisib, significant inhibition of cell proliferation and biomass was observed in the triple combination compared to the DMSO control group . The addPE) is lower than the observed inhibition results (OE) in all cell lines .The Bliss prediction effects were calculated based on the results of proliferation and biomass inhibition. For the BI-3406-based triple inhibitor combination, the Bliss predicted that inhibition (ll lines . When foOE and PE were between 20 and 43% (proliferation) and 23 and 36% (biomass) (OE and PE were between 3 and 15% (proliferation) and 4 and 16% (biomass)  and 8%, 32% (biomass), respectively  . The combiomass) . In addiectively . In the Apoptosis/necrosis assays were performed on ASPC-1, BXPC-3, CAPAN-1, and MIA PACA-2 cells after exposure to BI-3406-based inhibitor combinations and MIA PACA-2 after exposure to sotorasib-based inhibitor combinations. Compared to the DMSO control group, Annexin V/propidium iodide (PI) double staining revealed a significant increase in induced apoptosis/necrosis, when using the triple-inhibitor combinations . These tFurthermore, the microscopic evaluation at 100× magnification of Pappenheim stained samples indicated that the cells clearly demonstrated signs of cell death, including numerous vacuoles in the cytoplasm, splitting or breaking up of the nucleoli (karyorrhexis), protrusions of the plasma membrane, and apoptotic bodies, as well as morphological deformation . These mDifferential expression analysis revealed several genes that were differentially regulated in triple combination-treated cells, when compared with the DMSO control exposed cells. For the combination of BI-3406 with trametinib and buparlisib, 587 DEGs were identified in ASPC-1 cells, 423 DEGs in BXPC-3 cells, 1191 DEGs in CAPAN-1 cells, and 1259 DEGs were identified in MIA PACA-2 cells . Of thesFor the sotorasib triple combination, only MIA PACA-2 cells were analyzed. Compared to the DMSO control group, 928 DEGs were identified in MIA PACA-2 . ComprehIn order to assess the effect of inhibitor combinations on PDAC cell lines, GO and KEGG pathway analysis was performed on all of the DEGs selected in result 3.6 for each cell line.For the BI-3406 triple inhibitor combination, GO and KEGG enrichment analysis demonstrated different results in different PDAC cell lines. The number of GO terms, including the biological process (BP), the cellular component (CC), and the molecular function (MF), as well as the number of KEGG pathways caused by BI-3406 triple inhibitor combination treatment, are displayed in Further analysis of the GO term function revealed that in the PDAC cell lines, DEGs were involved in regulating the immune system, cell adhesion, cell migration, localization, locomotion, and response to stimulus in biological process, cell membrane, and extracellular functions in cellular components, as well as cytokine binding in molecular functions. KEGG pathway enrichment analysis identified nine overlapping pathways, which were involved in cancer, cellular community, cardiovascular disease, and immune regulation and directly acting on PI3K/AKT and TNF pathways . FurtherFor the sotorasib combination, 928 DEGs in MIA PACA-2 were involved in 849 BP, 39 CC, and 63 MF; KEGG analysis revealed that DEGs were enriched in 58 pathways, which are mainly associated with cancer, signal transduction, and the immune system .Comparing the GO terms and KEGG pathway enrichment analysis of MIA PACA-2 in the two inhibitor combinations did not reveal major differences. The GO term demonstrated that both inhibitor combinations were involved in similar cellular functions in MIA PACA-2. KEGG analysis revealed that both inhibitor combinations were involved in immune regulation, signal transduction , metabolic activity, and cancer pathways . The BI-3406 triple combination additionally participated in the MAPK pathway; however, this effect was not observed in the sotorasib triple combination .KRAS mutations are the most common mutations in PDAC patients and are characterized by poor prognosis and resistance to general treatment [KRAS G12C mutation, exhibited the expected inhibitory efficacy in MIA PACA-2, and significantly inhibited cell proliferation and biomass even at very low concentrations (0.005 μM). At the same time, sotorasib at 10 μM exhibited a partial inhibitory effect on other tested PDAC cell lines, except for T3M4 (KRAS Q61H). The cell proliferation and biomass decreased by 32–50% and 24–41%, respectively. However, in T3M4, minimal inhibition of cell proliferation and biomass was observed at all the concentrations tested. This may be due to the fact that the Q61H mutation has the lowest intrinsic GTPase activity and requires less upstream signaling to maintain a GTP-bound status [reatment ,14. Althd status . In a prd status . The resd status . The incd status ,38. Althd status . CombineKRAS-mutated and wild-type PDAC cell lines, which is in agreement with previous reports, both confirming the synergistic effect of BI-3406 and trametinib [KRAS-mutated and wild-type cell lines, this double-inhibitor combination was also effective in the BXPC-3 cells. For the combination of BI-3406 and buparlisib, a synergistic effect was only observed in MIA PACA-2. Since buparlisib does not reduce p-MEK and p-ERK levels, it is highly likely that it fails to activate the negative feedback loop, resulting in a small synergistic effect [Using the multi-KRAS mutation inhibitor BI-3406, our results were comparable to those previously reported in 2D cultures . The bioametinib . This isametinib . Since tc effect .The double-inhibitor combination based on sotorasib also displayed a synergistic effect, but mainly in MIA PACA-2 cells. Since RAS directly forms a complex with PI3K to further activate the PI3K signaling pathway, inhibition of these two proteins greatly reduces the activation of this pathway and might explain the synergistic effect of these two inhibitors ,40,41,42The efficacy of the triple-inhibitor combination of BI-3406, trametinib, and buparlisib was significantly stronger than that of the double-inhibitor combination in ASPC-1, BXPC-3, and MIA PACA-2 cells. However, in CAPAN-1 cells, there was no significant improvement in the triple-inhibitor combination versus the double-inhibitor combination of buparlisib and trametinib. Moreover, the KEGG pathway enrichment analysis revealed that in CAPAN-1, the RAS pathway was not affected by the triple therapy, while the enrichment of DEGs in the RAS pathway was observed in the other three cell lines. In addition, a single application of BI-3406 did not significantly inhibit the proliferation and biomass of CAPAN-1. Although BI-3406 has previously been reported to achieve good inhibitory effects on KRAS G12V-mutated NSCLC cell lines, this antitumor effect appears to be poor for PDAC cell lines . This suALDOA, IL6, IL6R, EGF, VEFG, PDK-1, ENO1, etc.) were all associated with the hypoxia inducible factor-1 (HIF-1) pathway, suggesting that both combinations can act on the HIF-1 pathway. Several studies have shown that HIF-1 is associated with tumor growth in a variety of cancers, including PDAC [The BI-3406 triple inhibitor combination modulated immunity, cell adhesion, migration, and targeted cancer pathways in all four cell lines. This indicates that this inhibitor combination can directly influence the pathophysiology of tumor cells, but might also indirectly inhibit the growth of PDAC cells by modulating the immune system, as well as cell-to-cell interactions. Furthermore, we observed that in all four cell lines, both triple inhibitor combinations regulated DEGs, which are involved in the response to hypoxia. These genes (ing PDAC . Inhibiting PDAC ,45. The Altogether, our current study demonstrates the antiproliferative effects of KRAS inhibitors alone or in combination with downstream inhibitors in PDAC cell lines in vitro. Moreover, the dose of each inhibitor was greatly reduced when used in combination, thereby reducing the side effects of the inhibitor. The KRAS::SOS1 inhibitor BI-3406 was able to enhance the antiproliferative effect of downstream inhibitors in the KRAS wild-type, KRAS G12C, and KRAS G12D mutant cell lines, but not for the KRAS G12V mutant cell lines. The KRAS G12C inhibitor sotorasib mainly enhanced the anti-proliferative effect of downstream inhibitors in KRAS G12C mutant cell lines.KRAS statuses. The two triple combinations also demonstrated extraordinary effects in enhancing inhibitor efficacy and reducing inhibitor dose. These data emphasize the importance of KRAS as a therapeutic target for PDAC and validate two different mechanisms of KRAS inhibition and its interaction with downstream pathway inhibitors. The current study provides novel ideas for the drug treatment of PDAC; however, in vivo experiments and clinical trials are still needed to observe the real efficacy and adverse reactions of these inhibitors and inhibitor combinations for the treatment of PDAC.Our current study demonstrates the effects of two KRAS inhibitors, BI-3406 and sotorasib, as monotherapy for PDAC. This provides evidence for a potential extended application of sotorasib in non-KRAS G12C mutated PDAC and the application of BI-3406 as a multi-KRAS mutated inhibitor in PDAC. In addition, these two KRAS inhibitors act synergistically or additively with downstream pathway inhibitors, when reducing cell proliferation and biomass in PDAC cell lines with different