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FGFR2 genomic alterations are observed in 10–20% of cholangiocarcinoma (CCA). Although FGFR2 fusions are an important actionable target, FGFR2 protein expression has not been thoroughly characterized. To evaluate FGFR2 protein expression in cholangiocarcinoma harboring FGFR2 genomic alterations. FGFR2 protein expression was evaluated in 99 CCA cases with two different antibodies. FGFR2 genomic alterations were confirmed via next-generating sequencing (NGS) or FISH. Primary objective was to determine the specificity and sensitivity of FGFR2 immunohistochemistry staining for detecting FGFR2 genomic alterations. Secondary objectives included overall FGFR2 immunohistochemistry staining in CCA patients, and evaluation of whether FGFR2 expression correlates with clinical outcomes including overall survival (OS), progression-free survival (PFS), and time-to-tumor recurrence (TTR). Immunohistochemistry staining with two antibodies against FGFR2, FPR2-D, and clone 98706 showed high accuracy (78.7% and 91.9%) and specificity (82.9% and 97.7%), and moderate sensitivity (53.9% and 57.1%), respectively, when compared with the standard methods for detecting FGFR2 genomic alterations. In a median follow-up of 72 months, there were no statistically significant differences in OS, PFS, and TTR, for patients with positive or negative FGFR2 staining. FGFR2 protein expression by immunohistochemistry has high specificity and therefore could be used to imply the presence of FGFR2 genomic alterations in the context of a positive test. In the case of a negative test, NGS or FISH would be necessary to ascertain cases with FGFR2 genomic alterations.

Gamma-interferon-inducible lysosomal thiol reductase (GILT) is critical for MHC class II restricted presentation of multiple melanoma antigens. There is variable GILT protein expression in malignant melanocytes in melanoma specimens. High GILT mRNA expression in melanoma specimens is associated with improved overall survival, before the advent of immune checkpoint inhibitors (ICI). However, the association of GILT in metastatic melanoma with survival in patients treated with ICI and the cell type expressing GILT associated with survival have not been determined. Using RNA sequencing datasets, high GILT mRNA expression in metastatic melanoma specimens was associated with improved progression-free and overall survival in patients treated with ICI. A clinical dataset of metastatic melanoma specimens was generated and annotated with clinical information. Positive GILT immunohistochemical staining in antigen presenting cells and melanoma cells was observed in 100% and 65% of metastatic melanoma specimens, respectively. In the subset of patients treated with ICI in the clinical dataset, high GILT protein expression within melanoma cells was associated with improved overall survival. The association of GILT mRNA and protein expression with survival was independent of cancer stage. These studies support that high GILT mRNA expression in bulk tumor samples and high GILT protein expression in melanoma cells is associated with improved survival in ICI-treated patients. These findings support further investigation of GILT as a biomarker to predict the response to ICI.

Accurate prioritization of immunogenic neoantigens is key to developing personalized cancer vaccines and distinguishing those patients likely to respond to immune checkpoint inhibition. However, there is no consensus regarding which characteristics best predict neoantigen immunogenicity, and no model to date has both high sensitivity and specificity and a significant association with survival in response to immunotherapy. We address these challenges in the prioritization of immunogenic neoantigens by (1) identifying which neoantigen characteristics best predict immunogenicity; (2) integrating these characteristics into an immunogenicity score, the NeoScore; and (3) demonstrating a significant association of the NeoScore with survival in response to immune checkpoint inhibition. One thousand random and evenly split combinations of immunogenic and nonimmunogenic neoantigens from a validated dataset were analyzed using a regularized regression model for characteristic selection. The selected characteristics, the dissociation constant and binding stability of the neoantigen:MHC class I complex and expression of the mutated gene in the tumor, were integrated into the NeoScore. A web application is provided for calculation of the NeoScore. The NeoScore results in improved, or equivalent, performance in four test datasets as measured by sensitivity, specificity, and area under the receiver operator characteristics curve compared with previous models. Among cutaneous melanoma patients treated with immune checkpoint inhibition, a high maximum NeoScore was associated with improved survival. Overall, the NeoScore has the potential to improve neoantigen prioritization for the development of personalized vaccines and contribute to the determination of which patients are likely to respond to immunotherapy.

Hepatitis B virus (HBV) genotype E (HBV‐E) accounts for the majority of chronic hepatitis B (CHB) infections in West Africa. We aimed to determine factors associated with HBV‐E‐induced hepatocellular carcinoma (HCC) in West Africa. Data on patients from Burkina Faso who were hepatitis B surface antigen positive (HBsAg+) and had CHB were analyzed. HBV viral load and hepatitis B e antigen (HBeAg) status were measured in 3,885 individuals with CHB without HCC (CHB HCC−) and 59 individuals with CHB with HCC (CHB HCC+). HBV genotyping was performed for 364 subjects with CHB HCC− and 41 subjects with CHB HCC+. Overall, 2.5% of the CHB HCC− group was HBeAg+ compared with 0% of the CHB HCC+ group. Of the 364 patients who were CHB HCC− with available genotyping, the frequencies of HBV genotypes E and C/E were 70.3% and 12.9%, respectively. Age (odds ratio [OR] for older age, 1.08; 95% confidence interval [CI], 1.06‐1.10 per 1‐year increase in age), male sex (OR, 2.03; 95% CI, 1.11‐3.69), and HBV viremia (OR, 1.48; 95% CI, 1.31‐1.67 per 1 log10 IU/mL) were each associated with HCC diagnosis. Patients with genotype E had a lower HBeAg prevalence (6.3% vs. 14.9%), lower HBV viral load, and higher prevalence of cirrhosis (14.5% vs. 4.8%) than patients with genotype C/E. Conclusion: HBV‐E is the most common circulating strain (70.3%) in West African patients. HCC was associated with older age, male sex, and high HBV viral load. It is expected that these results will further inform guidance on clinical management of HBV infection in West Africa.

ICTBioMed, the International Consortium for Technology in Biomedicine is a consortium for biomedical collaborative research and the consortium partners believe in decentralization and democratization of science. The consortium works on strategies to develop ready-to-use research initiatives bridging the gap between research, implementation and ready-to-use science. The consortium has identified real science use cases with the help of the collaborative partners to get access of shared data and methods using the principles of science gateways. ICTBioMed has set up an NCIP Hub instance based upon the HUBzero framework and started using it for community research activities. It is an environment for scientific collaboration where researchers can access and take advantage of a variety of resources shared by others. The NCIP Hub supports members with shared access to data, tools, and standards across the cancer research community. Cancer researchers, in principle, have access to a wide range of data and software tools but in practice, however, they make limited use of these resources. Barriers that restrict access include the need to install software locally, inconsistent user interfaces, security concerns and poor documentation. ICTBioMed is developing a cancer science gateway that will greatly simplify analysis in cancer imaging research by offering a single point of entry and a unified interface for multiple tools, databases, and image repositories widely used by the community. The consortium aims at borderless research with a common goal to solve real life biomedical problems by facilitating collaborative research.

Simple Summary Melanoma is a deadly skin cancer, and the incidence of melanoma is rising. Chemoprevention, using small molecule drugs to prevent the development of cancer, is a key strategy that could reduce the burden of melanoma on society. The long-term goal of our study is to develop a gene signature biomarker of progression from nevi to melanoma. We found that a small number of genes can distinguish nevi from melanoma and identified shared genes and immune-related pathways that are associated with progression from nevi to melanoma across independent datasets. This study demonstrates (1) a novel approach to aid melanoma chemoprevention trials by using a gene signature as a surrogate endpoint and (2) the feasibility of determining a gene signature biomarker of melanoma progression. Abstract There is a need to identify molecular biomarkers of melanoma progression to assist the development of chemoprevention strategies to lower melanoma incidence. Using datasets containing gene expression for dysplastic nevi and melanoma or melanoma arising in a nevus, we performed differential gene expression analysis and regularized regression models to identify genes and pathways that were associated with progression from nevi to melanoma. A small number of genes distinguished nevi from melanoma. Differential expression of seven genes was identified between nevi and melanoma in three independent datasets. C1QB, CXCL9, CXCL10, DFNA5 (GSDME), FCGR1B, and PRAME were increased in melanoma, and SCGB1D2 was decreased in melanoma, compared to dysplastic nevi or nevi that progressed to melanoma. Further supporting an association with melanomagenesis, these genes demonstrated a linear change in expression from benign nevi to dysplastic nevi to radial growth phase melanoma to vertical growth phase melanoma. The genes associated with melanoma progression showed significant enrichment of multiple pathways related to the immune system. This study demonstrates (1) a novel application of bioinformatic approaches to aid clinical trials of melanoma chemoprevention and (2) the feasibility of determining a gene signature biomarker of melanomagenesis.

There is a need to identify biomarkers of melanoma progression to assist the development of chemoprevention strategies to lower melanoma incidence. In this study, we assessed the feasibility of creating a molecular signature for melanomagenesis using three publicly available RNA sequencing and microarray expression datasets. We performed differential expression and regularized regression analyses across nevi and melanoma samples to identify consistent genes associated with melanomagenesis. The regularized regression models demonstrated that a small number of genes could successfully distinguish between nevi and melanoma, providing evidence for the feasibility of creating a molecular signature. Differential expression analysis identified consistent upregulation of C1QB, CXCL9, CXCL10, DFNA5 (GSDME), FCGR1B, and PRAME in melanoma and consistent downregulation of SCGB1D2 in melanoma compared to nevi. Additionally, each of these genes demonstrated a linear association with the progression from benign nevi to dysplastic nevi, to radial growth phase melanoma to vertical growth phase melanoma, providing additional evidence for their role in melanomagenesis. Subsequent pathway analysis demonstrated significant enrichment of immune-related pathways among the differentially expressed genes. Overall, this study 1) demonstrates the feasibility of creating a gene signature for melanomagenesis and 2) highlights genes and pathways of interest for melanoma progression. We are in the process of generating a new dataset with benign nevi, dysplastic nevi, and melanoma with which to build and validate a molecular signature of melanoma. Citation Format: Elizabeth S. Borden, Anngela C. Adams, Kenneth H. Buetow, Melissa A. Wilson, Julie E. Bauman, Clara Curiel-Lewandrowski, H.-H. Sherry Chow, Bonnie J. LaFleur, Karen Taraszka Hastings. Shared gene expression and immune pathway changes associated with progression from nevi to melanoma [abstract]. In: Proceedings of the Second Biennial NCI Meeting: Translational Advances in Cancer Prevention Agent Development (TACPAD); 2022 Sep 7-9. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_2): Abstract nr A006.

PURPOSEThis investigation sough to evaluate the prognostic value of pre-treatment ctDNA in metastatic biliary tract cancers (BTC) treated with platinum based first-line chemotherapy treatment.METHODSWe performed a retrospective analysis of 67 patients who underwent ctDNA testing before platinum-based chemotherapy for first-line treatment for metastatic BTC. For analysis we considered the detected gene with highest variant allele frequency (VAF) as the dominant clone allele frequency (DCAF). Results of ctDNA analysis were correlated with patients’ demographics, progression-free survival (PFS) and overall survival (OS).RESULTSThe median age of patients was 67 years (27-90). 54 (80.6%) of 67 patients evaluated had intrahepatic cholangiocarcinoma; seven had extrahepatic cholangiocarcinoma and six gallbladder cancers. 46 (68.6%) of the patients were treated with cisplatin plus gemcitabine, 16.4% of patients received gemcitabine and other platinum (carboplatin or oxaliplatin) combinations while 15% of patients were treated on a clinical trial with gemcitabine and cisplatin plus additional agents (CX4945, PEGPH20 or nab-paclitaxel). TP53, KRAS, FGFR2, ARID1A, STK11 and IDH1 were the genes with highest frequency as DCAF. Median DCAF was 3% (0-97%). DCAF >3% was associated with worse OS (median OS: 10.8 vs. 18.8 months, p=0.032). Stratifying DCAF in quartiles, DCAF>10% was significantly related to worse PFS (median PFS: 3 months, p=0.014) and worse OS (median OS: 7.0 months, p=0.001). Each 1% increase in ctDNA was associated with a hazard ratio of 13.1 in OS when adjusting for subtypes, metastatic sites, size of largest tumor, age, sex, and CA19-9.CONCLUSIONDCAF at diagnosis of advanced BTC can stratify patients who have worse outcomes when treated with upfront platinum-based chemotherapy. Each increase in %ctDNA decrease survival probabilities.

In post-industrial settings, apolipoprotein E4 (APOE4) is associated with increased cardiovascular and neurological disease risk. However, the majority of human evolutionary history occurred in environments with higher pathogenic diversity and low cardiovascular risk. We hypothesize that in high-pathogen and energy-limited contexts, the APOE4 allele confers benefits by reducing innate inflammation when uninfected, while maintaining higher lipid levels that buffer costs of immune activation during infection. Among Tsimane forager-farmers of Bolivia (N = 1266, 50% female), APOE4 is associated with 30% lower C-reactive protein, and higher total cholesterol and oxidized LDL. Blood lipids were either not associated, or negatively associated with inflammatory biomarkers, except for associations of oxidized LDL and inflammation which were limited to obese adults. Further, APOE4 carriers maintain higher levels of total and LDL cholesterol at low body mass indices (BMIs). These results suggest that the relationship between APOE4 and lipids may be beneficial for pathogen-driven immune responses and unlikely to increase cardiovascular risk in an active subsistence population., eLife digest Genes contain the instructions needed for a cell to make molecules called proteins, which perform various roles in the body. Different variants of a gene can affect how the protein works, and in some cases, can increase a person’s risk to develop certain diseases. For example, people who carry a version of the apolipoprotein E gene called APOE4 have a greater risk of developing Alzheimer’s disease or heart disease. Individuals with two copies of this genetic variant have a 45% higher risk of heart disease and 12 times higher risk of Alzheimer’s disease. Studies in industrialized countries suggest this increased risk may be the result of higher cholesterol and inflammation in people with APOE4. But if APOE4 is harmful, why does it continue to be so common worldwide? One potential explanation is that APOE4, which has been around since before modern humans, may be beneficial in some contexts. Cholesterol is essential for many vital tasks in the body. In physically demanding environments where parasitic infections are common – conditions similar to those experienced by early humans – APOE4 might be beneficial. Under those circumstances, having more cholesterol might help fuel metabolic activities, fight infections, or reduce inflammation caused by infections. Garcia et al. investigated the link between the APOE4 genetic variant, cholesterol and inflammation in 1,266 Indigenous Tsimane people from 80 villages in Bolivia. Tsimane people live an active lifestyle foraging and farming for food. Parasite infections are a common problem in their communities, but obesity rates are very low. Garcia et al. found that Tsimane people with at least one copy of the APOE4 have lower levels of inflammation and higher levels of cholesterol than those who have two copies of the APOE3 version of the gene. Very lean people with APOE4 had especially high levels of the so called “bad” low density lipoprotein (LDL) cholesterol compared to people with APOE3 only. However, in this situation, storing a little extra cholesterol may not be so bad. The findings contradict other studies that have linked obesity to higher LDL levels and APOE4 to higher levels of inflammation. For the majority of human history, humans lived in more physically strenuous and calorically restrictive environments, with less access to clean water. Garcia et al. suggest that the harmful effects of APOE4 seen in studies in more industrialized societies – where people tend to be more sedentary and have less exposure to pathogens – may reflect a mismatch between a person’s environment and their genes. More studies that capture the diversity of environmental conditions under which people live will help clarify the role of APOE4 health and disease.

Biliary tract cancers (BTCs) are uncommon, but highly lethal, gastrointestinal malignancies. Gemcitabine/cisplatin is a standard-of-care systemic therapy, but has a modest impact on survival and harbors toxicities, including myelosuppression, nephropathy, neuropathy, and ototoxicity. Whereas BTCs are characterized by aberrations activating the cyclinD1/cyclin-dependent kinase (CDK)4/6/CDK inhibitor 2a/retinoblastoma pathway, clinical use of CDK4/6 inhibitors as monotherapy is limited by lack of validated biomarkers, diffident preclinical efficacy, and development of acquired drug resistance. Emerging studies have explored therapeutic strategies to enhance the antitumor efficacy of CDK4/6 inhibitors by the combination with chemotherapy regimens, but their mechanism of action remains elusive.Here, we report in vitro and in vivo synergy in BTC models, showing enhanced efficacy, reduced toxicity, and better survival with a combination comprising gemcitabine/cisplatin and CDK4/6 inhibitors. Furthermore, we demonstrated that abemaciclib monotherapy had only modest efficacy attributable to autophagy-induced resistance. Notably, triplet therapy was able to potentiate efficacy through elimination of the autophagic flux. Correspondingly, abemaciclib potentiated ribonucleotide reductase catalytic subunit M1 reduction, resulting in sensitization to gemcitabine.As such, these data provide robust preclinical mechanistic evidence of synergy between gemcitabine/cisplatin and CDK4/6 inhibitors and delineate a path forward for translation of these findings to preliminary clinical studies in advanced BTC patients.

Accurate prioritization of immunogenic neoantigens is key to developing personalized cancer vaccines and distinguishing those patients likely to respond to immune checkpoint inhibition. However, there is no consensus regarding which characteristics best predict neoantigen immunogenicity, and no model to date has both high sensitivity and specificity and a significant association with survival in response to immunotherapy. We address these challenges in the prioritization of immunogenic neoantigens by 1) identifying which neoantigen characteristics best predict immunogenicity, 2) integrating these characteristics into an immunogenicity score, NeoScore, and 3) demonstrating an improved association of the NeoScore with response to immune checkpoint inhibition compared to mutational burden. One thousand random and evenly split combinations of immunogenic and non-immunogenic neoantigens from a validated dataset were analyzed using a regularized regression model for characteristic selection. The selected characteristics, the dissociation constant and binding stability of the neoantigen:MHC class I complex and expression of the mutated gene in the tumor, were integrated into the NeoScore. A web application is provided for calculation of the NeoScore. The NeoScore results in improved, or equivalent, performance in four test datasets as measured by sensitivity, specificity, and area under the receiver operator characteristics curve compared to previous models. Among cutaneous melanoma patients treated with immune checkpoint inhibition, a high NeoScore had a greater association with improved survival compared to mutational burden. Overall, the NeoScore has the potential to improve neoantigen prioritization for the development of personalized vaccines and contribute to the determination of which patients are likely to respond to immunotherapy.

Rationale: Surgery results in rapid and progressively severe exposure of tumor tissue to hypoxia, up to the point of complete removal, but its effect on tumor gene expression is not well characterized. We document the molecular effects of surgery on primary breast cancer tumor with a serial tissue sampling strategy, including an intra-operative sample. Methods: We included treatment naïve, non-metastatic breast cancer patients and sampled tumor and tumour adjacent normal tissue during surgery at three time points: beginning of surgery (Sample A), after half the tumor circumference had been devascularized (Sample B) and from completely resected tumor (Sample C). Patients were divided into two groups: discovery and validation. Tumor or adjacent normal samples from the discovery group underwent whole transcriptome paired-end sequencing (RNA-Seq) generating at least 50 million reads using next generation sequencing. Findings from discovery group were evaluated in a validation group using qRT-PCR and Nanostring nCounter gene expression profiling. Results: 81 breast cancer patients were eligible for this study of whom 46 with at least 1 quality passed sample at time-point A, B or C comprised the discovery group whose samples underwent RNA-seq. Validation group comprised two subsets: 35 independent patients (8 patients’ samples qRT-PCR, data included here; 27 patients’ samples nCounter gene expression, data will be presented) and 17 patients from discovery group whose samples also underwent nCounter gene expression profiling (data will be presented). Individual patient based analyses for A vs B vs C in discovery group revealed 249 significantly de-regulated genes in at least 20% of patients, in at least one comparison (AvB or BvC or AvC). Genes involved in stress response (FOSB, FOS, JUN, JUNB, DUSP1, CYR61, EGR1-3, ATF3, RGS1, RGS2), inflammation (IL20, IL8, SOCS3, GABRP, PIGR, NR4A1-2, CCL2- 3, CCL21, CCL14, CCL18-19, CXCL2, CXCL9-10, CXCL14), invasion & migration (PTGS2, MMP9-13), lipid metabolism (CD36, LIPE, TAT, FABP4, PLIN1, PLIN4, LPL, LEP), epithelial markers (KRT5, KRT7, KRT14-17, KRT23, KRT6A-6B) and cellular differentiation(SOX10, LRP2, S100A2, S100A7-A9, S100B, S100P) were significantly deregulated at time-point B versus A and many of these genes were also significantly deregulated in C versus A comparison, suggesting sustained deregulation through surgical resection. Replicative analysis involving comparison of tumors grouped by time points (A vs B vs C) identified 192 genes uniquely de-regulated in any one of the 3 comparisons, of which 42 overlapped with patient-wise analysis. These 42 genes included all the AP-1 transcription factor network signaling genes, epithelial markers (KRT14, KRT6A), inflammation related genes (SOCS3, PIGR, GABRP, NR4A1, NR4A2), lipid metabolism related genes (PLIN1) and cellular differentiation & cell fate related genes (SOX10, LRP2). Pathway analyses will be presented. qRT-PCR on paired samples in 8 independent patients validated the differential expression of eight genes in either AvB or AvC or both comparisons: FOS, FOSB, JUNB, DUSP1, RGS1, NR4A2, ZFP36, andMMP13. Comparison of biopsy (corresponding to A) with surgical samples (corresponding to C) in 6 cancer types (breast, cervical, lymphoma, mesothelioma, esophageal and sarcoma) in TCGA studies showed statistically significant de-regulation of 11 AP-1 transcription factor network signaling related genes in at least 4 cancers, further validating our experimental results. Conclusions: Our experiment, uniquely including an intra-operative tumor sample, shows that surgical removal induces a conserved stress response in the primary tumor that is potentially capable of bestowing metastatic capability on the tumor cells. Citation Format: Sudeep Gupta, Rohan Chaubal, Nilesh Gardi, Sunil Pachakar, Dimple Bhatia, Poonam Gera, Nita Nair, Shalaka Joshi, Vani Parmar, Purvi Thakkar, Garvit Chitkara, Rasika Kadam, Sejal Gujarathi, Harsh Oza, Rohini Hawaldar, Vaibhav Vanmali, Kenneth Buetow, Amit Dutt, Rajendra Badwe. Molecular effects of surgical resection on primary breast tumor [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-05-01.

BackgroundSex-differences in cancer occurrence and mortality are evident across tumor types; men exhibit higher rates of incidence and often poorer responses to treatment. Targeted approaches to the treatment of tumors that account for these sex-differences require the characterization and understanding of the fundamental biological mechanisms that differentiate them. Hepatocellular Carcinoma (HCC) is the second leading cause of cancer death worldwide, with the incidence rapidly rising. HCC exhibits a male-bias in occurrence and mortality, but previous studies have failed to explore the sex-specific dysregulation of gene expression in HCC.MethodsHere, we characterize the sex-shared and sex-specific regulatory changes in HCC tumors in the TCGA LIHC cohort using combined and sex-stratified differential expression and eQTL analyses.ResultsBy using a sex-specific differential expression analysis of tumor and tumor-adjacent samples, we uncovered etiologically relevant genes and pathways differentiating male and female HCC. While both sexes exhibited activation of pathways related to apoptosis and cell cycle, males and females differed in the activation of several signaling pathways, with females showing PPAR pathway enrichment while males showed PI3K, PI3K/AKT, FGFR, EGFR, NGF, GF1R, Rap1, DAP12, and IL-2 signaling pathway enrichment. Using eQTL analyses, we discovered germline variants with differential effects on tumor gene expression between the sexes. 24.3% of the discovered eQTLs exhibit differential effects between the sexes, illustrating the substantial role of sex in modifying the effects of eQTLs in HCC. The genes that showed sex-specific dysregulation in tumors and those that harbored a sex-specific eQTL converge in clinically relevant pathways, suggesting that the molecular etiologies of male and female HCC are partially driven by differential genetic effects on gene expression.ConclusionsSex-stratified analyses detect sex-specific molecular etiologies of HCC. Overall, our results provide new insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and provide a framework for future studies on sex-biased cancers.

In post-industrial settings,APOE4is associated with increased cardiovascular and neurological disease risk. However, the majority of human evolutionary history occurred in environments with higher pathogenic diversity and low cardiovascular risk. We hypothesize that in high-pathogen and energy-limited contexts, theAPOE4allele confers benefits by reducing baseline innate inflammation when uninfected, while maintaining higher lipid levels that buffer costs of immune activation during infection. Among Tsimane forager-farmers of Bolivia (N=1266),APOE4is associated with 30% lower C-reactive protein, and higher total cholesterol and oxidized-LDL. Blood lipids were either not associated, or negatively associated with inflammatory biomarkers, except for associations of oxidized-LDL and inflammation which were limited to obese adults. Further,APOE4carriers maintain higher levels of total and LDL cholesterol at low BMIs. These results suggest the relationship betweenAPOE4and lipids is likely beneficial for pathogen-driven immune responses, and unlikely to increase cardiovascular risk in an active subsistence population.

Biliary tract cancers (BTCs) are uncommon but highly lethal gastrointestinal malignancies. Gemcitabine/cisplatin is a standard-of-care (SOC) systemic therapy, but has a modest impact on survival and harbor toxicities including myelosuppression, nephropathy, neuropathy and ototoxicity. While BTCs are characterized by aberrations activating the cyclinD1-CDK4/6-CDKN2A-RB pathway, clinical use of CDK4/6 inhibitors as monotherapy is limited by lack of validated biomarkers, diffident pre-clinical efficacy and development of acquired drug resistance. Emerging studies have explored therapeutic strategies to enhance the anti-tumor efficacy of CDK4/6 inhibitors by combination with chemotherapy-regimens but their mechanism of action remains elusive. Here, we report in vitro and in vivo synergy in BTC models, showing enhanced efficacy, reduced toxicity and better survival with a combination comprising gemcitabine/cisplatin and CDK4/6 inhibitors. Furthermore, we demonstrated that abemaciclib monotherapy had only modest efficacy due to autophagy induced resistance. Notably, triplettherapy was able to potentiate efficacy through elimination of the autophagic flux. Correspondingly, abemaciclib potentiated RRM1 reduction, resulting in sensitization to gemcitabine. Conclusions: As such, these data provide robust pre-clinical mechanistic evidence of synergy between gemcitabine/cisplatin and CDK4/6 inhibitors, and delineate a path forward for translation of these findings to preliminary clinical studies in advanced BTC patients.

We hypothesize that, ancestrally, sex-specific immune modulation evolved to facilitate survival of the pregnant person in the presence of an invasive placenta and an immunologically challenging pregnancy - an idea we term the 'pregnancy compensation hypothesis' (PCH). Further, we propose that sex differences in immune function are mediated, at least in part, by the evolution of gene content and dosage on the sex chromosomes, and are regulated by reproductive hormones. Finally, we propose that changes in reproductive ecology in industrialized environments exacerbate these evolved sex differences, resulting in the increasing risk of autoimmune disease observed in females, and a counteracting reduction in diseases such as cancer that can be combated by heightened immune surveillance. The PCH generates a series of expectations that can be tested empirically and that may help to identify the mechanisms underlying sex differences in modern human diseases.

Current approaches to prioritization of tumor-specific neoantigens have low sensitivity and specificity, directly impacting the development of personalized vaccines and the prediction of immunotherapy efficacy. Many biological features have been suggested to contribute to accurate neoantigen prioritization. We calculated predictors for a comprehensive set of biological features for presentation and recognition of neoantigens on a validated neoantigen dataset. One thousand random, evenly-split combinations of immunogenic and non-immunogenic neoantigens were analyzed using penalized regression (lasso) for variable selection. This analysis isolated the peptide:MHC class I dissociation constant, binding stability, and mRNA expression as the key predictive variables of tumor-specific neoantigens. Based on these results, we fit a logistic regression model and tested its performance on three independent datasets. We compared our results to the models by Wells et al., Łuksza et al., and Zhou et al. Our model has improved performance as measured by the area under the receiver operator curve (AUC) compared to Łuksza and Zhou et al. Our model selects the same terms as Wells et al. and performs equivalently. However, it has the advantage of providing a predicted immunogenicity score for each individual neoantigen. Individual antigen approaches are useful for prioritizing vaccine candidates and predicting response to immunotherapy. Future work will focus on the application of this predictor to patient response to immunotherapy.

Summary Purpose The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. Methods The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive ( http://cancerimagingarchive.net ) of 102 GBM patients using the VASARI scoring system. The patients’ clinical and genetic data were obtained from the TCGA website ( http://www.cancergenome.nih.gov/ ). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. Results The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679 ± 0.068, Akaike's information criterion 566.7, P Conclusion A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.

The field of computer modeling and simulation of biological systems is rapidly advancing, backed by significant progress in the fields of experimentation techniques, computer hardware, and programming software. The result of a simulation may be delivered in several ways, from numerical results, through graphs of the simulated run, to a visualization of the simulation. The vision of an in-silico experiment mimicking an in-vitro or in-vivo experiment as it is viewed under a microscope is appealing but technically demanding and computationally intensive. Here, we report “Cell Studio,” a generic, hybrid platform to simulate an immune microenvironment with biological and biophysical rules. We use game engines—generic programs for game creation which offer ready-made assets and tools—to create a visualized, interactive 3D simulation. We also utilize a scalable architecture that delegates the computational load to a server. The user may view the simulation, move the “camera” around, stop, fast-forward, and rewind it and inject soluble molecules into the extracellular medium at any point in time. During simulation, graphs are created in real time for a broad view of system-wide processes. The model is parametrized using a user-friendly Graphical User Interface (GUI). We show a simple validation simulation and compare its results with those from a “classical” simulation, validated against a “wet” experiment. We believe that interactive, real-time 3D visualization may aid in generating insights from the model and encourage intuition about the immunological scenario.

An ever-expanding understanding of the molecular basis of the more than 200 unique diseases collectively called cancer, combined with efforts to apply these insights to clinical care, is forming the foundation of an era of personalized medicine that promises to improve cancer treatment. At the same time, these extraordinary opportunities are occurring in an environment of intense pressure to contain rising healthcare costs. This environment presents a challenge to oncology research and clinical care, because both are becoming progressively more complex and expensive, and because the current tools to measure the cost and value of advances in care (e.g., comparative effectiveness research, cost-effectiveness analysis, and health technology assessments) are not optimized for an ecosystem moving toward personalized, patient-centered care. Reconciling this tension will be essential to maintaining progress in a cost-constrained environment, especially because emerging innovations in science (e.g., increasing identification of molecular biomarkers) and in clinical process (implementation of a learning healthcare system) hold potential to dramatically improve patient care, and may ultimately help address the burden of rising costs. For example, the rapid pace of innovation taking place within oncology calls for increased capability to integrate clinical research and care to enable continuous learning, so that lessons learned from each patient treated can inform clinical decision making for the next patient. Recognizing the need to define the policies required for sustained innovation in cancer research and care in an era of cost containment, the stakeholder community must engage in an ongoing dialogue and identify areas for collaboration. This article reflects and seeks to amplify the ongoing robust discussion and diverse perspectives brought to this issue by multiple stakeholders within the cancer community, and to consider how to frame the research and regulatory policies necessary to sustain progress against cancer in an environment of constrained resources. Clin Cancer Res; 20(5); 1081–6. ©2014 AACR.

Multiple myeloma (MM) is the second most common hematological cancer, accounting for 2% of all cancer deaths. MM is associated with a poor prognosis, with a 5-year overall survival of 50.7%. While the introduction of new therapies in the last decade has nearly doubled the survival rate, most patients still experience a relapse. To this end, The Multiple Myeloma Research Foundation developed the CoMMpass study. CoMMpass is a longitudinal clinical trial aimed at accelerating the discovery of more targeted treatments for MM. Clinical parameters and tumor specimens are collected from each of the 1,147 patients at baseline and through the eight-year observation period. To identify genetic determinants of clinical outcomes, each tumor specimen is characterized by Whole Genome, Exome and Transcriptome sequencing. This work has uncovered a number of somatic mutations and copy number alterations associated with tumor progression and response to therapy as well as a novel classification of MM into 12 distinct subtypes based on gene expression. However, the contribution of germline genetic variation to gene expression changes and MM outcome remains poorly understood. Genome-wide association studies have identified germline variants associated with MM risk, indicating inherited genetic susceptibility. Furthermore, MM exhibits a disparity in occurrence and mortality between the sexes and ethnicities, men and African Americans being in at a higher risk than women or those of European ancestry. To better understand the genetic and biological basis of MM predisposition, we utilize a systems genomics approach to examine non-coding inherited and somatic genetic effects on gene expression and MM outcome. Data from 607 CoMMpass participants with available WGS and RNAseq from normal blood and baseline tumor specimens were used to map cis-acting eQTLs. We identified 3929 genes with at least one cis-acting eQTL. Furthermore, we discovered a number of regulatory variants with differential effects on tumor gene expression between the sexes and ethnicities, as well as eQTLs overlapping MM GWAS risk loci, providing regulatory mechanisms connecting these loci to MM. Finally, we discovered eQTLs that modulate overall survival in patients with MM. To validate these putatively regulatory loci in vitro, we utilized MM cell lines and CRISPR activation/interference, successfully altering the expression of the target genes. These and analyses integrating germline and somatic variants, gene expression, and clinical outcomes support the development of personalized medicine approaches for the better treatment of MM. Citation Format: Heini M. Natri, Austin Gutierrez, Bianca Argente, Melissa Wilson Sayres, Kenneth Buetow, Nicholas Banovich. Genetic effects on gene expression and survival in patients with multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4352.

Complex Adaptive Systems (CAS) are ubiquitous and composed of many interacting “agents” that exhibit independent properties and behaviors that function together with their environment to produce emergent properties. Emergence and emergent properties cannot be predicted by isolated understanding of these interacting agents/components; but can be demonstrated by observing the outcomes associated with dynamically changing interacting components of a CAS. Obviously, evolution also plays a key role in driving emergence as a defining feature of biological CAS. Biological CAS are highly heterogeneous and complex, both within and across broad scales of time and space. Biological CAS are also non-linear which means that predicting outcomes is difficult. Moreover, it is impossible to “fix” a CAS, rather the identification of leverage points that can alter the trajectory of the system to achieve desired outcomes is a more logical approach. Until recently, systems of such high dimensionality were not sufficiently tractable to understand and apply CAS principles to a disease as complex as cancer. However, recent progress in the development of advanced technologies such as computation, machine learning, artificial intelligence, and modeling portend a day when cancer will be viewed and managed as a CAS. These “big data” tools offer new and innovative opportunities to mine, manage, manipulate, model and simulate cancer to derive the information needed to manage it as a CAS. In terms of applying these principles at least one approach to treating cancer, immunotherapy, suggests that achieving a future state where cancer is viewed through the lens of CAS is well underway. Immunotherapy represents a paradigm shift in cancer therapy in that it targets the immune system, which is a quintessential CAS. When immunotherapy is successful the outcome is a “homeostatic reset” of what is an extraordinarily complex interaction between cancer and the immune system. Together these two complex systems comprise a CAS that promises to re-define how we treat and prevent cancer. A variety of immunotherapeutics (dominated by checkpoint inhibitors) have produced durable responses (possible cures) in a few patients against some cancers. These agents essentially block signals that the tumor employs to keep the immune system from recognizing and killing the cancer. However, the interaction of cancer and the immune system is a dynamic CAS that will ultimately require a detailed understanding of the cellular and microenvironmental changes that occur in patients in response to specific immunotherapeutic interventions. The challenges we face are significant including: identifying responders/non-responders; determining doses; predicting and controlling toxicities; developing rational combinations; and creating new targeted systems-based therapies. Fortunately, many of these challenges can be met by defining the “states” produced by some of the defining alterations observed in responsive and non-responsive patients including “omics” alterations, types of immune cells, temporal relationships, immune activation, humoral factors, etc. Although early, models and platforms to describe, annotate, model and simulate these systems alterations are emerging. In the past several years, we have developed a modeling platform that permits the study of the immune system and its interaction with cancer. “Cell Studio” is an immune-modeling engine that seeks to examine cancer and the immune response as a dynamic CAS by using real world data on the immune system and cancer to develop and inform computational models. Cell Studio permits the user to conduct in silico experiments of defined time and complexity. It combines agent-based and mathematical modeling approaches to capture multiscale dynamics within the immune system. The engine permits user creation of multiple different types of immune cells each with different classes of properties including different collections of cell surface receptors at different concentrations and affinities as well as the capacity to release and respond to cytokines. Multiple compartments corresponding to different body niches (e.g. lymph node, tumor environment) can be created. Mathematical models govern phenomena such as diffusion and cell tracking of cytokine gradients. As a CAS, based on a finite number of “rules” the system is self-organizing and can display emergent properties. User defined therapeutic interventions such as drug administration can be incorporated to assess the system’s response. Cell Studio is implemented using a gaming platform so that the in silico experiments can be visualized in 3D - in real time if desired. This permits researchers to perform experiments similar to those done using biologic model systems and visualizing the results. Like most video gaming platforms, different user views, overviews, individual cell movement, etc. are available and real-time as well as cumulative statistical outputs are captured and displayed. Unlike biologic model systems the simulations can be time reversed to identify, visualize, and manipulate key events. Experimentation using the Cell Studio modeling engine shows that it can recapitulate the longitudinal events in biologic model systems. Additionally, it can recover “immunophenotypes” observed in human studies of immunotherapy in cancer. It is anticipated that the in silico modeling can augment current biologic modeling strategies - especially since it can be run with numbers of replicates of virtual experiments that are not practical with biologic model systems. Additionally, it promises to assist in the rationale to develop combinatorial interventions hitting multiple immune targets and in understanding factors that modulate successful outcomes. In summary, the implications of viewing, studying and developing strategic approaches to fundamentally understand the cancer - immune system CAS are profound. Cell Studio is a next generation novel and powerful approach to analyze and model specific components of this dynamic and integrated CAS for the benefit of patients. Citation Format: Anna D. Barker, Kenneth Buetow. Viewing cancer as a complex adaptive system and managing immunotherapy as “homeostatic reset” [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY36-03.

One recently identified subtype of pediatric B-precursor acute lymphoblastic leukemia (ALL) has been termed BCR-ABL1–like or Ph-like because of similarity of the gene expression profile to BCR-ABL1 positive ALL suggesting the presence of lesions activating tyrosine kinases, frequent alteration of IKZF1, and poor outcome. Prior studies demonstrated that approximately half of these patients had genomic lesions leading to CRLF2 overexpression, with half of such cases harboring somatic mutations in the Janus kinases JAK1 and JAK2. To determine whether mutations in other tyrosine kinases might also occur in ALL, we sequenced the tyrosine kinome and downstream signaling genes in 45 high-risk pediatric ALL cases with either a Ph-like gene expression profile or other alterations suggestive of activated kinase signaling. Aside from JAK mutations and 1 FLT3 mutation, no somatic mutations were found in any other tyrosine kinases, suggesting that alternative mechanisms are responsible for activated kinase signaling in high-risk ALL.

It is difficult for existing methods to quantify, and track the constant evolution of cancers due to high heterogeneity of mutations. However, structural variations associated with nucleotide number changes show repeatable patterns in localized regions of the genome. Here we introduce SPKMG, which generalizes nucleotide number based properties of genes, in statistical terms, at the genome-wide scale. It is measured from the normalized amount of aligned NGS reads in exonic regions of a gene. SPKMG values are calculated within OncoTrack. SPKMG values being continuous numeric variables provide a statistical metric to track DNA level changes. We show that SPKMG measures of cancer DNA show a normative pattern at the genome-wide scale. The analysis leads to the discovery of core cancer genes and also provides novel dynamic insights into the stage of cancer, including cancer development, progression, and metastasis. This technique will allow exome data to also be used for quantitative LOH/CNV analysis for tracking tumour progression and evolution with a higher efficiency.

The cancer genome is abnormal genome, and the ability to monitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using >200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA). With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.

Personalized cancer therapy offers the promise of delivering the right treatments to the right patients to improve patient outcomes and quality of life, while reducing exposure to ineffective therapies and the cost of cancer care. Realizing this promise depends in large part on our ability to generate timely and sufficiently detailed information regarding factors that influence treatment response. Generating this evidence through the traditional physician investigator-initiated clinical trial system has proved to be challenging, given poor recruitment rates and low compliance with requests for biospecimen collection. As a result, our current understanding of treatment response is inadequate, particularly for cancer therapies that have been in use for many years. Patient-initiated study participation may offer a new model for evidence generation that capitalizes on strong patient interest in furthering research to inform better and more tailored cancer therapies. In this approach, patients are engaged and recruited directly by the sponsor of an Institutional Review Board–approved study, and patients subsequently drive the participation of their health care providers to facilitate collection of required data and tissue samples. The ultimate goal of these studies is to generate evidence of sufficient quality to inform regulatory decisions (i.e., labeling changes for marketed therapies to reflect populations most likely to respond) and treatment selection. Here, we describe a hypothetical prospective observational study in non–small cell lung cancer that could serve as a model for patient-initiated study participation applied to understand molecular determinants of treatment response. Key elements discussed include study design, patient engagement, and data/biospecimen collection and management principles. Clin Cancer Res; 17(21); 6651–7. ©2011 AACR.

We sequenced 120 candidate genes in 187 high-risk childhood B-precursor acute lymphoblastic leukemias, the largest pediatric cancer genome sequencing effort reported to date. Integrated analysis of 179 validated somatic sequence mutations with genome-wide copy number alterations and gene expression profiles revealed a high frequency of recurrent somatic alterations in key signaling pathways, including B-cell development/differentiation (68% of cases), the TP53/RB tumor suppressor pathway (54%), Ras signaling (50%), and Janus kinases (11%). Recurrent mutations were also found in ETV6 (6 cases), TBL1XR1 (3), CREBBP (3), MUC4 (2), ASMTL (2), and ADARB2 (2). The frequency of mutations within the 4 major pathways varied markedly across genetic subtypes. Among 23 leukemias expressing a BCR-ABL1-like gene expression profile, 96% had somatic alterations in B-cell development/differentiation, 57% in JAK, and 52% in both pathways, whereas only 9% had Ras pathway mutations. In contrast, 21 cases defined by a distinct gene expression profile coupled with focal ERG deletion rarely had B-cell development/differentiation or JAK kinase alterations but had a high frequency (62%) of Ras signaling pathway mutations. These data extend the range of genes that are recurrently mutated in high-risk childhood B-precursor acute lymphoblastic leukemia and highlight important new therapeutic targets for selected patient subsets.

The occurrence and mortality rates of most types of cancer are significantly higher in males than in females. Environmental risk factors may explain some portion of the sex-bias, but cellular and molecular differences are also likely important. The sexes differ in their endocrinological and immunological functions, genetic makeup, and in their response to some forms of chemotherapy and immunotherapy. However, sex differences are rarely considered in the development of cancer therapies. Targeted approaches to the treatment of male and female tumors will require characterizing and understanding the fundamental biological mechanisms that differentiate them. Hepatocellular carcinoma (HCC) is the second leading cause of cancer death worldwide. HCC is influenced by genetic susceptibility, environmental factors such as oncogenic viral infections, metabolic syndrome, and alcohol use, and shaped by numerous biological processes, resulting in a high degree of genetic and transcriptional heterogeneity. HCC incidence in the US has doubled in the last 3 decades, attributable to increased rates of obesity. HCC exhibits sex bias in occurrence with 2.1-fold higher rate in HBV+ males, 1.9-fold in HCV+ females. While sex-bias in HCC is partly attributed to sex-specific differences in risk behaviors and environmental exposure, the relationship of these factors and sex as a biological variable has not been systematically investigated. Moreover, most GWAS and eQTL studies as yet have failed to explore the role of sex-specific impacts of regulatory variants. Here, we utilize an integrative systems genetic approach to investigate the mechanisms involved in HCC. By sex-specific analyses of genotypic, transcriptomic, and phenotypic data from GTEx, TCGA, and ICGC datasets, we have produced a detailed characterization of regulatory networks underlying HCC occurrence and susceptibility. As mis-mapping of sequence reads along the sex chromosomes may increase the rate of false negatives when accounting for sex-biased gene expression, we have utilized custom sex-specific reference genome sequence alignments. We have carried out gene and isoform level analyses of differential expression, pathway enrichment analyses, cis- and trans-eQTL mapping, and de novo network analyses to identify the mechanisms underlying HCC oncogenesis in each sex. Substantial sex-biased gene expression was detected in normal liver tissue, as well as tumor-adjacent and tumor tissues. Analyses of paired tumor and tumor-adjacent samples imply that distinct oncogenic pathways shape HCC in males and females. We found considerable differences in significant eQTLs between sexes. Constructed networks further elucidate the sex-specific regulatory mechanisms of HCC oncogenesis. Our results illustrate the highly sex-dependent genomic architecture of HCC, with implications for future studies and targeted therapies. Citation Format: Heini M. Natri, Melissa Wilson Sayres, Kenneth Buetow. Sex-specific genomic architecture and regulatory mechanisms underlying hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 396.

To implement personalized medicine successfully, multidisciplinary teams of collaborating scientists must manage and analyze vast quantities of genomic and clinical outcomes data in a cohesive and integrated way. This process is complex and not supported by the existing IT infrastructure and tools available to most researchers. To address these needs, the National Cancer Institute initiated the cancer Biomedical Informatics Grid initiative in 2004, to develop and deploy the interoperable IT infrastructure and tools needed to help basic and clinical researchers manage and share these data. Now, the cancer Biomedical Informatics Grid is being deployed to cancer centers and other biomedical research organizations across the USA and around the world, facilitating collaborative research that will ultimately lead to improved patient outcomes.

Many researchers believe that personalized medicine is a potential solution to the well-known challenges facing the pharmaceutical industry worldwide today. Achieving the full promise of personalized medicine requires a new, systems approach to biomedicine that will only be enabled by novel, interoperable IT infrastructures that facilitate simpler data access, data sharing and enhanced collaboration. This article will describe how the technology developed in the cancer Biomedical Informatics Grid(®) (caBIG(®)) program (http://cabig.cancer.gov/) from the National Cancer Institute (NCI) is already enabling many research organizations to implement personalized medicine approaches for their basic and clinical research programs.

Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient's tumor. Here, we present Repository of Molecular Brain Neoplasia Data (Rembrandt), a cancer clinical genomics database and a Web-based data mining and analysis platform aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. To date, Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising ∼566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. Data can be queried and visualized for a selected gene across all data platforms or for multiple genes in a selected platform. Additionally, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-anomaly pairs to facilitate the discovery of novel biomarkers and therapeutic targets. We believe that Rembrandt represents a prototype of how high-throughput genomic and clinical data can be integrated in a way that will allow expeditious and efficient translation of laboratory discoveries to the clinic. (Mol Cancer Res 2009;7(2):157–67)

The Pathway Interaction Database (PID, "http://pid.nci.nih.gov":http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the U.S. National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists, and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.

Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas ( TCGA) pilot project aims to assess the value of large- scale multi- dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas - the most common type of primary adult brain cancer - and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol- 3- OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.

Systematic investigations of genetic changes in tumors are expected to lead to greatly improved understanding of cancer etiology. To meet the analytical challenges presented by such studies, we developed the Cancer Genome WorkBench (http://cgwb.nci.nih.gov), the first computational platform to integrate clinical tumor mutation profiles with the reference human genome. A novel heuristic algorithm, IndelDetector, was developed to automatically identify insertion/deletion (indel) polymorphisms as well as indel somatic mutations with high sensitivity and accuracy. It was incorporated into an automated pipeline that detects genetic alterations and annotates their effects on protein coding and 3D structure. The ability of the system to facilitate identifying genetic alterations is illustrated in three projects with publicly accessible data. Mutagenesis in tumor DNA replication leading to complex genetic changes in the EGFR kinase domain is suggested by a novel deletion–insertion combination observed in paired tumor–normal lung cancer resequencing data. Automated analysis of 152 genes resequenced by the SeattleSNPs group was able to identify 91% of the 1251 indel polymorphisms discovered by SeattleSNPs. In addition, our system discovered 518 novel indels in this data set, 451 of which were found to be valid by manual inspection of sequence traces. Our experience demonstrates that CGWB not only greatly improves the productivity and the accuracy of mutation identification, but also, through its data integration and visualization capabilities, facilitates identification of underlying genetic etiology.