MondoA drives malignancy in B-ALL through enhanced adaptation to metabolic stress.

Cancer cells are in most instances characterized by rapid proliferation and uncontrolled cell division. Hence, they must adapt to proliferation-induced metabolic stress through intrinsic or acquired antimetabolic stress responses to maintain homeostasis and survival. One mechanism to achieve this is reprogramming gene expression in a metabolism-dependent manner. MondoA (also known as Myc-associated factor X-like protein X-interacting protein [MLXIP]), a member of the MYC interactome, has been described as an example of such a metabolic sensor. However, the role of MondoA in malignancy is not fully understood and the underlying mechanism in metabolic responses remains elusive. By assessing patient data sets, we found that MondoA overexpression is associated with worse survival in pediatric common acute lymphoblastic leukemia (ALL; B-precursor ALL [B-ALL]). Using clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) and RNA-interference approaches, we observed that MondoA depletion reduces the transformational capacity of B-ALL cells in vitro and dramatically inhibits malignant potential in an in vivo mouse model. Interestingly, reduced expression of MondoA in patient data sets correlated with enrichment in metabolic pathways. The loss of MondoA correlated with increased tricarboxylic acid cycle activity. Mechanistically, MondoA senses metabolic stress in B-ALL cells by restricting oxidative phosphorylation through reduced pyruvate dehydrogenase activity. Glutamine starvation conditions greatly enhance this effect and highlight the inability to mitigate metabolic stress upon loss of MondoA in B-ALL. Our findings give novel insight into the function of MondoA in pediatric B-ALL and support the notion that MondoA inhibition in this entity offers a therapeutic opportunity and should be further explored.

DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma.

Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability.

Genetic Screens Identify a Context-Specific PI3K/p27Kip1 Node Driving Extrahepatic Biliary Cancer.

Biliary tract cancer ranks among the most lethal human malignancies, representing an unmet clinical need. Its abysmal prognosis is tied to an increasing incidence and a fundamental lack of mechanistic knowledge regarding the molecular basis of the disease. Here, we show that the Pdx1-positive extrahepatic biliary epithelium is highly susceptible toward transformation by activated PIK3CAH1047R but refractory to oncogenic KrasG12D. Using genome-wide transposon screens and genetic loss-of-function experiments, we discover context-dependent genetic interactions that drive extrahepatic cholangiocarcinoma (ECC) and show that PI3K signaling output strength and repression of the tumor suppressor p27Kip1 are critical context-specific determinants of tumor formation. This contrasts with the pancreas, where oncogenic Kras in concert with p53 loss is a key cancer driver. Notably, inactivation of p27Kip1 permits KrasG12D-driven ECC development. These studies provide a mechanistic link between PI3K signaling, tissue-specific tumor suppressor barriers, and ECC pathogenesis, and present a novel genetic model of autochthonous ECC and genes driving this highly lethal tumor subtype. SIGNIFICANCE: We used the first genetically engineered mouse model for extrahepatic bile duct carcinoma to identify cancer genes by genome-wide transposon-based mutagenesis screening. Thereby, we show that PI3K signaling output strength and p27Kip1 function are critical determinants for context-specific ECC formation. This article is highlighted in the In This Issue feature, p. 2945.

SETBP1 overexpression acts in the place of class-defining mutations to drive FLT3-ITD-mutant AML.

Advances in cancer genomics have revealed genomic classes of acute myeloid leukemia (AML) characterized by class-defining mutations, such as chimeric fusion genes or in genes such as NPM1, MLL, and CEBPA. These class-defining mutations frequently synergize with internal tandem duplications in FLT3 (FLT3-ITDs) to drive leukemogenesis. However, ∼20% of FLT3-ITD-positive AMLs bare no class-defining mutations, and mechanisms of leukemic transformation in these cases are unknown. To identify pathways that drive FLT3-ITD mutant AML in the absence of class-defining mutations, we performed an insertional mutagenesis (IM) screening in Flt3-ITD mice, using Sleeping Beauty transposons. All mice developed acute leukemia (predominantly AML) after a median of 73 days. Analysis of transposon insertions in 38 samples from Flt3-ITD/IM leukemic mice identified recurrent integrations at 22 loci, including Setbp1 (20/38), Ets1 (11/38), Ash1l (8/38), Notch1 (8/38), Erg (7/38), and Runx1 (5/38). Insertions at Setbp1 led exclusively to AML and activated a transcriptional program similar, but not identical, to those of NPM1-mutant and MLL-rearranged AMLs. Guide RNA targeting of Setbp1 was highly detrimental to Flt3ITD/+/Setbp1IM+, but not to Flt3ITD/+/Npm1cA/+, AMLs. Also, analysis of RNA-sequencing data from hundreds of human AMLs revealed that SETBP1 expression is significantly higher in FLT3-ITD AMLs lacking class-defining mutations. These findings propose that SETBP1 overexpression collaborates with FLT3-ITD to drive a subtype of human AML. To identify genetic vulnerabilities of these AMLs, we performed genome-wide CRISPR-Cas9 screening in Flt3ITD/+/Setbp1IM+ AMLs and identified potential therapeutic targets, including Kdm1a, Brd3, Ezh2, and Hmgcr. Our study gives new insights into epigenetic pathways that can drive AMLs lacking class-defining mutations and proposes therapeutic approaches against such cases.

The early evolutionary landscape of osteosarcoma provides clues for targeted treatment strategies.

Osteosarcomas are aggressive primary tumors of bone that are typically detected in locally advanced stages; however, which genetic mutations drive the cancer before its clinical detection remain unknown. To identify these events, we performed longitudinal genome-sequencing analysis of 12 patients with metastatic or refractory osteosarcoma. Phylogenetic and molecular clock analyses were carried out next to identify actionable mutations, and these were validated by integrating data from additional 153 osteosarcomas and pre-existing functional evidence from mouse PDX models. We found that the earliest and thus clinically most promising mutations affect the cell cycle G1 transition, which is guarded by cyclins D3, E1, and cyclin-dependent kinases 2, 4, and 6. Cell cycle G1 alterations originate no more than a year before the primary tumor is clinically detected and occur in >90% and 50% of patients of the discovery and validation cohorts, respectively. In comparison, other cancer driver mutations could be acquired at any evolutionary stage and often do not become pervasive. Consequently, our data support that the repertoire of actionable mutations present in every osteosarcoma cell is largely limited to cell cycle G1 mutations. Since they occur in mutually exclusive combinations favoring either CDK2 or CDK4/6 pathway activation, we propose a new genomically-based algorithm to direct patients to correct clinical trial options. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Overactivation of the IGF signalling pathway in osteosarcoma: a potential therapeutic target?

Osteosarcoma is the most common primary malignant bone tumour in children and adolescents. More than a third of patients do not respond to standard therapy and urgently require alternative treatment options. Due to a high degree of inter- and intra-tumoural genomic heterogeneity and complexity, recurrent molecular alterations that could serve as prognostic predictors or therapeutic targets are still lacking in osteosarcoma. Copy number (CN) gains involving the IGF1R gene, however, have been suggested as a potential surrogate marker for treating a subset of patients with IGF1R inhibitors. In this study, we screened a large set of osteosarcomas and found specific CN gains of the IGF1R gene in 18 of 253 (7.1%) cases with corresponding IGF1R overexpression. Despite the discouraging results observed in clinical trials in other tumours so far, focusing only on selected patients with osteosarcoma that show evidence of IGF pathway activation might represent a promising new and innovative treatment approach.

Evolutionary routes and KRAS dosage define pancreatic cancer phenotypes.

The poor correlation of mutational landscapes with phenotypes limits our understanding of the pathogenesis and metastasis of pancreatic ductal adenocarcinoma (PDAC). Here we show that oncogenic dosage-variation has a critical role in PDAC biology and phenotypic diversification. We find an increase in gene dosage of mutant KRAS in human PDAC precursors, which drives both early tumorigenesis and metastasis and thus rationalizes early PDAC dissemination. To overcome the limitations posed to gene dosage studies by the stromal richness of PDAC, we have developed large cell culture resources of metastatic mouse PDAC. Integration of cell culture genomes, transcriptomes and tumour phenotypes with functional studies and human data reveals additional widespread effects of oncogenic dosage variation on cell morphology and plasticity, histopathology and clinical outcome, with the highest KrasMUT levels underlying aggressive undifferentiated phenotypes. We also identify alternative oncogenic gains (Myc, Yap1 or Nfkb2), which collaborate with heterozygous KrasMUT in driving tumorigenesis, but have lower metastatic potential. Mechanistically, different oncogenic gains and dosages evolve along distinct evolutionary routes, licensed by defined allelic states and/or combinations of hallmark tumour suppressor alterations (Cdkn2a, Trp53, Tgfß-pathway). Thus, evolutionary constraints and contingencies direct oncogenic dosage gain and variation along defined routes to drive the early progression of PDAC and shape its downstream biology. Our study uncovers universal principles of Ras-driven oncogenesis that have potential relevance beyond pancreatic cancer.

Loss of P53 Function Activates JAK2-STAT3 Signaling to Promote Pancreatic Tumor Growth, Stroma Modification, and Gemcitabine Resistance in Mice and Is Associated With Patient Survival.

BACKGROUND & AIMS: One treatment strategy for pancreatic ductal adenocarcinoma is to modify, rather than deplete, the tumor stroma. Constitutive activation of the signal transducer and activator of transcription 3 (STAT3) is associated with progression of pancreatic and other solid tumors. We investigated whether loss of P53 function contributes to persistent activation of STAT3 and modification of the pancreatic tumor stroma in patients and mice. METHODS: Stat3, Il6st (encodes gp130), or Trp53 were disrupted, or a mutant form of P53 (P53R172H) or transgenic sgp130 were expressed, in mice that developed pancreatic tumors resulting from expression of activated KRAS (KrasG12D, KC mice). Pancreata were collected and analyzed by immunohistochemistry, in situ hybridization, quantitative reverse-transcription polymerase chain reaction (qPCR), or immunoblot assays; fluorescence-activated cell sorting was performed to identify immune cells. We obtained frozen pancreatic tumor specimens from patients and measured levels of phosphorylated STAT3 and P53 by immunohistochemistry; protein levels were associated with survival using Kaplan-Meier analyses. We measured levels of STAT3, P53, ligands for gp130, interleukin 6, cytokines, sonic hedgehog signaling, STAT3 phosphorylation (activation), and accumulation of reactive oxygen species in primary pancreatic cells from mice. Mice with pancreatic tumors were given gemcitabine and a Janus kinase 2 (JAK2) inhibitor; tumor growth was monitored by 3-dimensional ultrasound. RESULTS: STAT3 was phosphorylated constitutively in pancreatic tumor cells from KC mice with loss or mutation of P53. Tumor cells of these mice accumulated reactive oxygen species and had lower activity of the phosphatase SHP2 and prolonged phosphorylation of JAK2 compared with tumors from KC mice with functional P53. These processes did not require the gp130 receptor. Genetic disruption of Stat3 in mice, or pharmacologic inhibitors of JAK2 or STAT3 activation, reduced fibrosis and the numbers of pancreatic stellate cells in the tumor stroma and altered the types of immune cells that infiltrated tumors. Mice given a combination of gemcitabine and a JAK2 inhibitor formed smaller tumors and survived longer than mice given control agents; the tumor stroma had fewer activated pancreatic stellate cells, lower levels of periostin, and alterations in collagen production and organization. Phosphorylation of STAT3 correlated with P53 mutation and features of infiltrating immune cells in human pancreatic tumors. Patients whose tumors had lower levels of phosphorylated STAT3 and functional P53 had significantly longer survival times than patients with high levels of phosphorylated STAT3 and P53 mutation. CONCLUSIONS: In pancreatic tumors of mice, loss of P53 function activates JAK2-STAT3 signaling, which promotes modification of the tumor stroma and tumor growth and resistance to gemcitabine. In human pancreatic tumors, STAT3 phosphorylation correlated with P53 mutation and patient survival time. Inhibitors of this pathway slow tumor growth and stroma formation, alter immune cell infiltration, and prolong survival of mice. Transcript profiling: ArrayExpress accession number: E-MTAB-3278.

Multiplexed pancreatic genome engineering and cancer induction by transfection-based CRISPR/Cas9 delivery in mice.

Mouse transgenesis has provided fundamental insights into pancreatic cancer, but is limited by the long duration of allele/model generation. Here we show transfection-based multiplexed delivery of CRISPR/Cas9 to the pancreas of adult mice, allowing simultaneous editing of multiple gene sets in individual cells. We use the method to induce pancreatic cancer and exploit CRISPR/Cas9 mutational signatures for phylogenetic tracking of metastatic disease. Our results demonstrate that CRISPR/Cas9-multiplexing enables key applications, such as combinatorial gene-network analysis, in vivo synthetic lethality screening and chromosome engineering. Negative-selection screening in the pancreas using multiplexed-CRISPR/Cas9 confirms the vulnerability of pancreatic cells to Brca2-inactivation in a Kras-mutant context. We also demonstrate modelling of chromosomal deletions and targeted somatic engineering of inter-chromosomal translocations, offering multifaceted opportunities to study complex structural variation, a hallmark of pancreatic cancer. The low-frequency mosaic pattern of transfection-based CRISPR/Cas9 delivery faithfully recapitulates the stochastic nature of human tumorigenesis, supporting wide applicability for biological/preclinical research.

CRISPR/Cas9 somatic multiplex-mutagenesis for high-throughput functional cancer genomics in mice.

Here, we show CRISPR/Cas9-based targeted somatic multiplex-mutagenesis and its application for high-throughput analysis of gene function in mice. Using hepatic single guide RNA (sgRNA) delivery, we targeted large gene sets to induce hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). We observed Darwinian selection of target genes, which suppress tumorigenesis in the respective cellular/tissue context, such as Pten or Cdkn2a, and conversely found low frequency of Brca1/2 alterations, explaining mutational spectra in human ICC/HCC. Our studies show that multiplexed CRISPR/Cas9 can be used for recessive genetic screening or high-throughput cancer gene validation in mice. The analysis of CRISPR/Cas9-induced tumors provided support for a major role of chromatin modifiers in hepatobiliary tumorigenesis, including that of ARID family proteins, which have recently been reported to be mutated in ICC/HCC. We have also comprehensively characterized the frequency and size of chromosomal alterations induced by combinatorial sgRNA delivery and describe related limitations of CRISPR/Cas9 multiplexing, as well as opportunities for chromosome engineering in the context of hepatobiliary tumorigenesis. Our study describes novel approaches to model and study cancer in a high-throughput multiplexed format that will facilitate the functional annotation of cancer genomes.

A conditional piggyBac transposition system for genetic screening in mice identifies oncogenic networks in pancreatic cancer.

Here we describe a conditional piggyBac transposition system in mice and report the discovery of large sets of new cancer genes through a pancreatic insertional mutagenesis screen. We identify Foxp1 as an oncogenic transcription factor that drives pancreatic cancer invasion and spread in a mouse model and correlates with lymph node metastasis in human patients with pancreatic cancer. The propensity of piggyBac for open chromatin also enabled genome-wide screening for cancer-relevant noncoding DNA, which pinpointed a Cdkn2a cis-regulatory region. Histologically, we observed different tumor subentities and discovered associated genetic events, including Fign insertions in hepatoid pancreatic cancer. Our studies demonstrate the power of genetic screening to discover cancer drivers that are difficult to identify by other approaches to cancer genome analysis, such as downstream targets of commonly mutated human cancer genes. These piggyBac resources are universally applicable in any tissue context and provide unique experimental access to the genetic complexity of cancer.

Suv39h-dependent H3K9me3 marks intact retrotransposons and silences LINE elements in mouse embryonic stem cells.

Heterochromatin is required to restrict aberrant expression of retrotransposons, but it remains poorly defined due to the underlying repeat-rich sequences. We dissected Suv39h-dependent histone H3 lysine 9 trimethylation (H3K9me3) by genome-wide ChIP sequencing in mouse embryonic stem cells (ESCs). Refined bioinformatic analyses of repeat subfamilies indicated selective accumulation of Suv39h-dependent H3K9me3 at interspersed repetitive elements that cover ∼5% of the ESC epigenome. The majority of the ∼8,150 intact long interspersed nuclear elements (LINEs) and endogenous retroviruses (ERVs), but only a minor fraction of the >1.8 million degenerate and truncated LINEs/ERVs, are enriched for Suv39h-dependent H3K9me3. Transcriptional repression of intact LINEs and ERVs is differentially regulated by Suv39h and other chromatin modifiers in ESCs but governed by DNA methylation in committed cells. These data provide a function for Suv39h-dependent H3K9me3 chromatin to specifically repress intact LINE elements in the ESC epigenome.

ChroMoS: an integrated web tool for SNP classification, prioritization and functional interpretation.

UNLABELLED: Genome-wide association studies and re-sequencing projects are revealing an increasing number of disease-associated SNPs, a large fraction of which are non-coding. Although they could have relevance for disease susceptibility and progression, the lack of information about regulatory regions impedes the assessment of their functionality. Here we present a web server, ChroMoS (Chromatin Modified SNPs), which combines genetic and epigenetic data with the goal of facilitating SNPs' classification, prioritization and prediction of their functional consequences. ChroMoS uses a large database of SNPs and chromatin states, but allows a user to provide his/her own genetic information. Based on the SNP classification and interactive prioritization, a user can compute the functional impact of multiple SNPs using two prediction tools, one for differential analysis of transcription factor binding (sTRAP) and another for SNPs with potential impact on binding of miRNAs (MicroSNiPer). AVAILABILITY: Web server, ChroMoS, is freely available at http://epicenter.immunbio.mpg.de/services/chromos.

MicroSNiPer: a web tool for prediction of SNP effects on putative microRNA targets.

MicroRNAs are short, approximately 22 nucleotide noncoding RNAs binding to partially complementary sites in the 3'UTR of target mRNAs. This process generally results in repression of multiple targets by a particular microRNA. There is substantial interest in methods designed to predict the microRNA targets and effect of single nucleotide polymorphisms (SNPs) on microRNA binding, given the impact of microRNA on posttranscriptional regulation and its potential relation to complex diseases. We developed a web-based application, MicroSNiPer, which predicts the impact of a SNP on putative microRNA targets. This application interrogates the 3'-untranslated region and predicts if a SNP within the target site will disrupt/eliminate or enhance/create a microRNA binding site. MicroSNiPer computes these sites and examines the effects of SNPs in real time. MicroSNiPer is a user-friendly Web-based tool. Its advantages include ease of use, flexibility, and straightforward graphical representation of the results. It is freely accessible at http://cbdb.nimh.nih.gov/microsniper.

DISC1 splice variants are upregulated in schizophrenia and associated with risk polymorphisms.

Disrupted-In-Schizophrenia-1 (DISC1) is a promising susceptibility gene for major mental illness, but the mechanism of the clinical association is unknown. We searched for DISC1 transcripts in adult and fetal human brain and tested whether their expression is altered in patients with schizophrenia and is associated with genetic variation in DISC1. Many alternatively spliced transcripts were identified, including groups lacking exon 3 (Delta3), exons 7 and 8 (Delta7Delta8), an exon 3 insertion variant (extra short variant-1, Esv1), and intergenic splicing between TSNAX and DISC1. Isoforms Delta7Delta8, Esv1, and Delta3, which encode truncated DISC1 proteins, were expressed more abundantly during fetal development than during postnatal ages, and their expression was higher in the hippocampus of patients with schizophrenia. Schizophrenia risk-associated polymorphisms [non-synonymous SNPs rs821616 (Cys704Ser) and rs6675281 (Leu607Phe), and rs821597] were associated with the expression of Delta3 and Delta7Delta8. Moreover, the same allele at rs6675281, which predicted higher expression of these transcripts in the hippocampus, was associated with higher expression of DISC1Delta7Delta8 in lymphoblasts in an independent sample. Our results implicate a molecular mechanism of genetic risk associated with DISC1 involving specific alterations in gene processing.

Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers.

There is substantial interest in methods designed to predict the effect of nonsynonymous single nucleotide polymorphisms (nsSNPs) on protein function, given their potential relationship to heritable diseases. Current state-of-the-art supervised machine learning algorithms, such as random forest (RF), train models that classify single amino acid mutations in proteins as either neutral or deleterious to function. However, it is frequently the case that the functional effect of a polymorphism on a protein resides between these two extremes. The utilization of classifiers that incorporate fuzzy logic provides a natural extension in order to account for the spectrum of possible functional consequences. We generated a dataset of single amino acid substitutions in human proteins having known three-dimensional structures. Each variant was uniquely represented as a feature vector that included computational geometry and knowledge-based statistical potential predictors obtained though application of Delaunay tessellation of protein structures. Additional attributes consisted of physicochemical properties of the native and replacement amino acids as well as topological location of the mutated residue position in the solved structure. Classification performance of the RF algorithm was evaluated on a training set consisting of the disease-associated and neutral nsSNPs taken from our dataset, and attributes were ranked according to their relative importance. Similarly, we evaluated the performance of adaptive neuro-fuzzy inference system (ANFIS). The utility of statistical geometry predictors was compared with that of traditional structural and evolutionary attributes employed by other researchers, revealing an equally effective yet complementary methodology. Among all attributes in our feature set, the statistical geometry predictors were found to be the most highly ranked. On the basis of the AUC (area under the ROC curve) measure of performance, the ANFIS and RF models were equally effective when only statistical geometry features were utilized. Tenfold cross-validation studies evaluating AUC, balanced error rate (BER), and Matthew's correlation coefficient (MCC) showed that our RF model was at least comparable with the well-established methods of SIFT and PolyPhen. The trained RF and ANFIS models were each subsequently used to predict the disease potential of human nsSNPs in our dataset that are currently unclassified (http://rna.gmu.edu/FuzzySnps/).

Statistical geometry approach to the study of functional effects of human nonsynonymous SNPs.

The ability to predict the effect of nonsynonymous SNPs (nsSNPs) on protein function is important for the success of genetic disease association studies. Here we present a statistical geometry approach to nsSNP classification based on Delaunay tessellation, whereby the impact of nsSNPs on protein function is correlated with the change in the four-body statistical potential (DeltaQ) of the protein caused by the amino acid substitution. We observed that the DeltaQ of polymorphic proteins with disease-associated nsSNPs (daSNPs) was on average significantly lower than the DeltaQ of the proteins with neutral SNPs (ntSNPs). Clustering amino acid substitutions into conservative and nonconservative groups, and using a three-letter alphabet based on side-chain polarity showed significantly lower DeltaQ in nonconservative changes to daSNPs and when hydrophobic residues were substituted by charged or by polar residues. We also found that the daSNPs in the protein core caused much lower DeltaQ than surface daSNPs. This approach demonstrates a strong correlation between the computed DeltaQ and SNP classification. Integration of our approach with the existing models will help achieve a more precise recognition of nsSNPs that underlie polygenic diseases. All of the programs were written in Java and are available from the authors upon request.