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Background: We previously reported the development and validation of an integrative Immunotherapy Response Score (IRS) algorithm, which integrates tumor mutation burden (TMB) and quantitative gene expression of PD-L1, PD-1, ADAM12 and TOP2A, to predict PD-1 or PD-L1 (together PD-[L]1) monotherapy (mono) benefit across solid tumors from routine formalin fixed paraffin embedded samples. Herein, we evaluated IRS performance for predicting pembrolizumab (pembro) mono benefit in a second, independent validation cohort. Methods: We identified patients from an ongoing observational trial (NCT03061305) integrating molecular profiling with real world treatment data who were 1) treated with systemic pembro mono, 2) not included in the original IRS cohort, and 3) had valid molecular profiling data to generate IRS. IRS status (IRS-High [H] vs. -Low [L] by the previously validated threshold; -H with increased benefit) association with pembro real world progression free survival (rwPFS) by time-to-next therapy was determined by Cox proportional hazard modeling (adjusting for age, gender, line of systemic therapy, and PD-(L)1 mono indicated (microsatellite instable high [MSI-H], TMB-H, or approved tumor type) vs. not indicated. The predictive nature of the IRS biomarker was assessed in the subset of >= 2nd line patients through a case cross-over analysis (pembro rwPFS vs. immediately preceding therapy line rwPFS) as assessed by likelihood ratio test (LRT). Results: Of the 176 patients in this independent validation cohort (from 25 tumor types), 78 (44%) patients were IRS-H, while 40 (23%) were TMB-H. Pembro mono rwPFS was significantly longer in IRS-H vs. IRS-L patients (median rwPFS 18.7 vs. 6.4 months, adjusted hazard ratio [aHR] 0.46, p=0.007), while PD-(L)1 mono indicated status was not associated with pembro mono rwPFS (indicated vs. not indicated aHR 1.47, p=0.26). In the case cross-over analysis of 55 (31%) patients treated with systemic therapy prior to pembro mono, pembro rwPFS was significantly longer than preceding therapy in IRS-H patients (median 18.7 vs. 5.5 months) but not IRS-L patients (median 4.9 vs. 5.4 months); the LRT for interaction between IRS status and pembro/prior therapy was significant (p=0.046); results in the TMB-L/non-MSI-H subset (n=45) of patients was similar (LRT p=0.025). Conclusion: These results confirm the pan-solid tumor PD-(L)1 monotherapy predictive nature of the IRS biomarker. Citation Format: Benjamin J. Bulen, Nickolay A. Khazanov, Laura E. Lamb, Daniel H. Hovelson, Kat Kwiatkowski, D. Bryan Johnson, Daniel R. Rhodes, Scott A. Tomlins. Validation of an integrative pan-solid tumor predictor of pembrolizumab monotherapy benefit. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4348.

Trastuzumab deruxtecan (Enhertu) is effective in "HER2 Low" breast cancer, defined by 1+ or 2+ expression by immunohistochemistry (IHC). Interest has now turned toward defining a sub-population of IHC 0+ tumors that may have HER2 expression below the limit of IHC detection/quantification and may thus also be responsive. We previously validated a high dynamic range HER2 RNA expression assay run as part of our comprehensive genomic profiling test, StrataNGS. Herein, we evaluated the HER2 RNA expression data together with copy number and clinical outcome data from the Strata Clinical Molecular Database (SCMD) in advanced breast cancer (n = 3,063) and other advanced solid tumors (n = 26,715). As expected, HER2 gene expression was significantly higher in tumors with DNA amplification (>=6 copies; median: 13.9 vs. 10.0 in log2 units; p < 1e-100). Despite similar copy number levels in amplified breast vs. other cancers (median: 21.8 vs. 19.8 copies), HER2 expression levels were ~2-fold higher (median: 14.5 vs. 13.5; p = 1.3e-10). Similarly, HER2 expression levels were higher in non-amplified breast vs. other cancers (median: 10.7 vs. 9.9; p In patients with available IHC data (n = 388), HER2 RNA expression trended with IHC across the 0-3+ range, however, while 3+ tumors had distinctly high RNA expression (median: 14.4), 0-2+ tumors had lower expression with overlapping distributions (median: 10.5, 10.9, 11.5, respectively), suggesting that 0-2+ tumors do not represent distinct biological groups, but rather a continuum of low expression. We defined a HER2 RNA Low threshold (>10.6), corresponding to the top 75% of IHC 1-2+ breast cancers. Importantly, at this threshold, nearly half (44.1%) of 0+ breast cancers were also classified as HER2 RNA Low. Additionally, 25.8% of all non-breast solid tumors were classified as HER2 RNA Low. Given that HER2 RNA High predicted benefit from 1st generation anti-HER2 therapies, future studies should consider HER2 RNA Low as an alternative biomarker to Her2 IHC Low, with the opportunity to further expand trastuzumab deruxtecan use into the IHC 0+ breast cancer population and potentially to additional solid tumors. Citation Format: Laura E. Lamb, Nickolay A. Khazanov, Daniel H. Hovelson, Kat Kwiatkowski, D. Bryan Johnson, Daniel R. Rhodes, Scott A. Tomlins. Evaluation of Her2 RNA expression as a potential predictive biomarker for anti-Her2 therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 968.

Despite the recent clinical success of antibody drug conjugates (ADC) in oncology, predictive biomarkers are lacking, potentially limiting their impact. Herein, we evaluated the ability of candidate biomarkers alone and in combination to predict objective response rates observed in solid tumor patients treated with the TROP2-targeted ADC, sacituzumab govitecan (SG), as determined in the IMMU-12-01 basket trial. We leveraged available next generation sequencing (NGS)-based molecular profiling data from an independent advanced solid tumor cohort (n = 23,968) and developed a multivariate biomarker algorithm that produced biomarker positive rates correlating with the objective response rates (ORR) observed in IMMU-12-01. Candidate biomarkers evaluated included TROP2 gene expression, proliferation (by gene expression) and tumor cellularity. Notably, while TROP2 gene expression was highly correlated with protein expression across 45 tumor types (r = 0.93), TROP2 gene expression alone did not significantly predict ORR across 9 tumor types (r = 0.40, p = 0.29). In contrast, a biomarker algorithm combining TROP2 and proliferation by gene expression with tumor cellularity strongly predicted response both when using tumor type-specific biomarker rates in a discovery cohort (r = 0.83, p = 0.006) and in an independent validation cohort (r = 0.82, p = 0.007). These results indicate that the multivariate biomarker accounts for 67% of the variability observed in response rates and may thus identify patients likely to benefit from SG. Among tumor types with objective responses in IMMU-12-01, biomarker positive rates ranged from 9.9% in colorectal cancer to 57.4% in bladder cancer. Additional tumor types with biomarker positive rates >30% included cancers of the head and neck, cervix, salivary gland, skin (non-melanoma) and ovary, all with positive biomarker rates >30%. Interestingly, most tumor types had biomarker positive rates >5%, suggesting the potential for a tumor type-agnostic approach to patient selection. Considering SG and other ADC’s mechanism of action, a plausible model for response is that (1) higher target expression increases ADC drug delivery, (2) higher tumor cellularity increases ADC bystander effect and (3) higher tumor cell proliferation increases tumor cell death. In summary, we uncovered a novel biomarker algorithm capable of predicting SG response across solid tumors that may be generalizable to ADCs as a class, with the potential to further optimize use and maximize benefit. Citation Format: Nickolay A. Khazanov, Laura E. Lamb, Daniel H. Hovelson, Kat Kwiatkowski, D. Bryan Johnson, Daniel R. Rhodes, Scott A. Tomlins. A multivariate biomarker predicts sacituzumab govitecan response in solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2171.

Next-generation sequencing (NGS) panels have recently been introduced to efficiently detect genetic variations in hematologic malignancies. Our aim was to evaluate the performance of the commercialized Oncomine™ myeloid research assay (OMA) for myeloid neoplasms. Certified reference materials and clinical research samples were used, including 60 genomic DNA and 56 RNA samples. NGS was performed using OMA, which enables the interrogation of 40 target genes, 29 gene fusions, and five expression target genes with five expression control genes by the Ion S5 XL Sequencer. The analyzed data were compared with clinical data using karyotyping, reverse transcription polymerase chain reaction (PCR), fluorescence in situ hybridization, Sanger sequencing, customized NGS panel, and fragment analysis. All targets of reference materials were detected except three (two ASXL1 and one CEBPA) mutations, which we had not expected OMA to detect. In clinical search samples, OMA satisfactorily identified DNA variants, including 90 single nucleotide variants (SNVs), 48 small insertions and deletions (indels), and eight FLT3 internal tandem duplications (ITDs) (Kappa agreement 0.938). The variant allele frequencies of SNVs and indels measured by OMA correlated well with clinical data, whereas those of FLT3-ITDs were significantly lower than with fragment analysis (P = 0.008). Together, OMA showed strong ability to identify RNA gene fusions (Kappa agreement 0.961), except one RUNX1-MECOM. The MECOM gene was highly expressed in all five samples with MECOM-associated rearrangements, including inv(3), t(3;3), and t(3;21). OMA revealed excellent analytical and potential clinical performance and could be a good replacement for conventional molecular tests.

Comprehensive genomic profiling (CGP) of tumor samples by next-generation sequencing is used to support clinical and translational research into the genetic variants that serve as biomarkers for diagnosis, prognosis and potential therapeutic response. However, as these assays grow in size to meet the expanding demands of users, it is challenging to maintain performance in the face of limited sample input necessitated by small sample volumes and to provide a simple and fast sample to report workflow with limited hands-on time. We, therefore, developed OCA Plus to meet user needs for a large CGP assay with excellent performance. Gene content was prioritized based on potential clinical relevance and variant prevalence in solid tumors. Over 500 genes were selected including genes in the indication statements of approved drug labels, clinical guidelines, and in the enrollment criteria of clinical trials. In addition, driver genes were selected in key pathways including DNA repair and immune checkpoint response. Amplicon design strategies were optimized accordingly for key hotspots, full coding sequences or copy number variation (CNV). The assay used Ion AmpliSeq™ technology with manual library preparation or automated templating on the Ion Chef™ System and sequencing on the Ion GeneStudio™ S5 platform. Twenty ng of purified DNA was routinely used as input. An automated tumor-only workflow for variant calling and sample quality reporting was provided within Ion Reporter™ Software. Streamlined access to reporting of variant relevance was enabled by Oncomine™ Reporter. In development studies of cancer cell line and formaldehyde-fixed, paraffin-embedded (FFPE) tumor samples, the assay displayed excellent uniformity (98% and 94%, respectively). Detection of single nucleotide variants and indels in cell lines and FFPE samples showed >95% sensitivity and PPV. Detection of CNV gain and loss in cell lines and FFPE samples showed >95% sensitivity and PPV. Assessment of tumor mutational burden (TMB) using publicly available whole-exome cancer sequencing data as well as test cell lines and FFPE samples showed high concordance with whole exome sequencing (R2 > 0.90). MSI sensitivity and specificity was >95% as tested using a diverse set of tumor samples. Targeted fusions were reported with 100% sensitivity and specificity when tested with commercially available controls. Total time from purified DNA to end of sequencing was < 2 days with < 3 hours of hands-on time and the time from post-sequencing to report generation was < 2 hours. Oncomine Comprehensive Assay (OCA Plus) was developed to support CGP and routine clinical research in oncology. The assay design and informatics workflow were optimized to support low input and rapid sample-to-report turn-around time, which will accelerate clinical and translational research. Citation Format: Vinay Mittal, Jennifer Kilzer, Dinesh Cyanam, Janice Au-Young, Santhoshi Bandla, Gary Bee, Sameh El-Difrawy, Aren Ewing, Rajesh Gottimukkala, Mohit Gupta, Nickolay Khazanov, Anelia Kraltcheva, Amir Marcovitz, Scott Myrand, Rose Putler, Yu-Ting Tseng, Warren Tom, Cristina Van Loy, James Veitch, Paul Williams, Elaine Wong-Ho, Huimin Xie, Chenchen Yang, Zheng Zang, Seth Sadis. Comprehensive genomic profiling of solid tumors for key targeted and immuno-oncology biomarkers using Ion Torrent NGS technology on the Oncomine Comprehensive Assay Plus (OCA Plus) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 179.

Background Gene fusions caused by chromosomal rearrangements play an important role in oncogenesis, the progression of cancer and the selection of targeted therapies. Next-generation sequencing (NGS) using RNA enables sensitive, specific and precise detection of potentially clinically relevant gene fusions, especially when the fusion breakpoint is known. We have developed an NGS solution appropriate for FFPE tissues to detect fusion biomarkers in routine clinical research. Methods Fusion content was selected based on prioritization of actionability, verified fusion isoforms reported in the literature and collaborations, as well as prevalence of solid tumor fusion driver genes. The assay was designed to use Ion AmpliSeq multiplex PCR chemistry using manual or automated library preparation, automated templating on the Ion Chef, and sequencing on the Ion Torrent GeneStudioTM S5 sequencing platform. The analysis was supported by fully automated analysis software that performs sample QC, read-filtering, fusion calling and reporting. Streamlined access to decision support software was enabled by Oncomine™ Reporter. Results Over 1200 targeted isoforms from over 50 known fusion driver genes were incorporated into the assay that included prominent fusion drivers such as ALK, RET, ROS1, NTRK1/2/3 and FGFR1/2/3, and intragenic fusion events in MET, EGFR, BRAF and AR. The assay also reported non-targeted fusion events in relevant driver genes by using a novel statistically significant expression imbalance algorithm comparing 5’- and 3’- end gene expression. A preliminary development study was performed using commercially available total RNA for a Tri-Fusion control and Seraseq™ Fusion RNA Mix v3 where all of the expected fusions were detected with 100% sensitivity and specificity. Results were also concordant when characterized FFPE tumor samples with known fusion targets were tested using the assay. Conclusions A comprehensive NGS assay was developed to support clinical research in oncology for detecting relevant RNA structural alterations from solid tumor FFPEs. An update on assay performance will be presented. Legal entity responsible for the study Thermo Fisher Scientific. Funding Has not received any funding. Disclosure V.K. Mittal: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. S.P. Myrand: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. D. Cyanam: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. P.D. Williams: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. G.G. Bee: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. A. Marcovitz: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. R. Gottimukkala: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. F. Hyland: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. C. Allen: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. E. Wong-Ho: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. S. Sadis: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. C. Van Loy: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. J. Kilzer: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. N. Khazanov: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific.

An appropriate structural superposition identifies similarities and differences between homologous proteins that are not evident from sequence alignments alone. We have coupled our Gaussian-weighted RMSD (wRMSD) tool with a sequence aligner and seed extension (SE) algorithm to create a robust technique for overlaying structures and aligning sequences of homologous proteins (HwRMSD). HwRMSD overcomes errors in the initial sequence alignment that would normally propagate into a standard RMSD overlay. SE can generate a corrected sequence alignment from the improved structural superposition obtained by wRMSD. HwRMSD’s robust performance and its superiority over standard RMSD are demonstrated over a range of homologous proteins. Its better overlay results in corrected sequence alignments with good agreement to HOMSTRAD. Finally, HwRMSD is compared to established structural alignment methods: FATCAT, SSM, CE, and Dalilite. Most methods are comparable at placing residue pairs within 2 A, but HwRMSD places many more residue pairs within 1 A, providing a clear advantage. Such high accuracy is essential in drug design, where small distances can have a large impact on computational predictions. This level of accuracy is also needed to correct sequence alignments in an automated fashion, especially for omics-scale analysis. HwRMSD can align homologs with low sequence identity and large conformational differences, cases where both sequence-based and structural-based methods may fail. The HwRMSD pipeline overcomes the dependency of structural overlays on initial sequence pairing and removes the need to determine the best sequence-alignment method, substitution matrix, and gap parameters for each unique pair of homologs.

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose ( www.csardock.org ). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein-ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (K(d) or K(i)) in Binding MOAD, augmented with entries from PDB bind. The final data set contains 343 diverse protein-ligand complexes and spans 14 pK(d). Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; Pearson R is limited to ~ 0.91 (Pearson R2 0.83) when fitting to the data set without over parameterizing. Pearson R is limited to ~ 0.83(Pearson R2 ~ 0.70) when scoring the data set with a method trained on outside data [corrected]. The details of how the data set was initially selected, and the process by which it matured to better fit the needs of the community are presented. Many groups generously participated in improving the data set, and this underscores the value of a supportive, collaborative effort in moving our field forward.

Introduction: Recurrent somatic alterations associated with pediatric, childhood, and young adult cancers have not been as intensively studied as those associated with adult cancers. Consequently, whole-exome and transcriptome approaches are still being used to support discovery efforts. However, due to several initiatives aimed at profiling genomic alterations associated with childhood cancers, a set of recurrent somatic alterations has been defined. To accelerate research in this area, we have developed a novel targeted next-generation sequencing (NGS) assay to detect relevant somatic alterations previously reported in these cancer types. Methods: The assay was developed using Ion AmpliSeq targeted sequencing technology to cover the major gene variants associated with childhood cancers, including both solid tumor and hematologic cancer types. Over 200 gene targets were included on the basis of consultation with expert pediatric oncologists, literature review of the recent pediatric cancer genomic publications, as well as inclusion of relevant markers from adult cancers that are also observed in childhood cancers. Variant classes include mutations, copy number variations, gene fusions, and gene expression. Mutations in 130 genes, copy number variants in 28 genes, and over 1,400 distinct fusion isoforms in 88 fusion driver genes are analyzed. Variant calling algorithms for both DNA and RNA were optimized and combined into a single Ion Reporter workflow. Results: The assay generated an average read depth of >3,000 reads per DNA amplicon with high uniformity (>95%), when up to 7 sample DNA-RNA pairs were analyzed with the 540 chip of the Ion S5 sequencing instrument. Minimal allele frequency detected for key hotspots was 5%. Sensitive and reproducible detection of CNV and fusion variants associated with pediatric solid tumors (EWSR1-FL1 and KIAA1549-BRAF fusions, MYC and EGFR amplification) and hematologic cancers (ETV6-RUNX1 and PML-RARA fusions) was demonstrated in orthogonally profiled FFPE, blood, and bone marrow samples. Performance was robust across sample types. Similar results were observed with manual and automated library preparation. Conclusions: A novel NGS assay, designed specifically for pediatric, childhood, and young adult cancers, and capable of detecting relevant DNA and RNA alterations from the same sample, was developed and validated. The assay is useful for characterizing relevant alterations in a wide range of cancers, including childhood leukemias and lymphomas as well as solid tumors including neuroblastoma, rhabdomyosarcoma, retinoblastoma, osteosarcoma, Ewing sarcoma, Wilms tumor, and brain and spinal cord tumors. A review of the analytical studies will be presented. Citation Format: Nickolay A. Khazanov, Chaitali Parikh, Habib Hamidi, Scott P. Myrand, Efren Ballesteros-Villagrana, Jingwei Ni, Paul D. Williams, Karen L. Clyde, Dinesh Cyanam, Armand Bankhead, III, Manimozhi Manivannan, Mark Tomilo, Susan Ewald, Jon K. Sherlock, Janice K. Au-Young, Jaclyn Biegel, Jonathan Buckley, Matthew Hiemenz, Dejerianne Ostrow, Alex Judkins, Xiaowu Gai, Tracy Busse, Alan Wayne, Deepa Bhojwani, Raca Gordana, Matthew Oberley, David Parham, Seth Sadis, Timothy Triche. Development of a next-generation sequencing (NGS) assay for pediatric, childhood, and young adult cancer research with comprehensive DNA and RNA variant detection [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr B45.

Title: Analytical performance of a novel next generation sequencing assay for myeloid cancers Nick Khazanov, Wally Zhang, Dinesh Cyanam, Scott P. Myrand, Denis Kaznadzey, Paul Williams, Vinay Mittal, Dan Mazur, Sihong Chen, Jason Wustman, Efren Ballesteros-Villagrana, Goutam Nistala, Jon Sherlock, Michael Hogan, Jim Veitch, John Bishop, Seth Sadis Introduction: Myeloid malignancies contain a diverse and heterogeneous set of genomic alterations that include recurrent somatic mutations in key driver genes as well as frequent and diagnostic chromosomal rearrangements that generate a wide array of gene fusion products. To support clinical and translational research into precision oncology strategies for myeloid cancers, a next-generation sequencing (NGS) assay was generated to detect common and relevant somatic alterations. Methods: To define gene targets that were recurrently altered in myeloid cancers and relevant for clinical and translational research, an extensive survey of investigators at hematology oncology research labs was performed. The gene targets identified by researchers were complemented by a comprehensive survey of literature and genomic databases. Clinical guidelines for myeloid diseases in the US and in Europe were reviewed to ensure representation of relevant alterations. A targeted Ion AmpliSeq panel was generated to support the detection of recurrent single-nucleotide variants, insertions/deletions, and gene fusions from blood or bone marrow samples. The panel was developed for manual or automated library preparation and sequencing on the Ion Torrent PGM or Ion S5 instruments. Results: The Oncomine™ Myeloid Research Assay gene panel included 58 genes and generated an average read depth of >2,000 reads per targeted amplicon with an average uniformity of >95%. Important GC-rich targets such as CEBPA generated sufficient balanced read depth to support variant detection. A cohort of samples positive for FLT3 alterations was analyzed and successful detection of FLT3-internal tandem repeat variants was demonstrated. Several gene fusion transcripts common to myeloid cancers were detected. Comparable results were observed on Ion Torrent PGM and Ion S5 instruments. Conclusions: A novel myeloid specific NGS assay capable of detecting relevant DNA and RNA alterations from the same sample was developed. The assay is useful for characterizing relevant alterations in a range of myeloid diseases including acute myeloid leukemia, myelodysplastic syndrome, myeloproliferative neoplasms, chronic myelogenous leukemia, chronic myelomonocytic leukemia, and juvenile myelomonocytic leukemia. A review of the analytical studies will be presented. Citation Format: Nickolay Khazanov, Wally Zhang, Dinesh Cyanam, Scott P. Myrand, Denis Kaznadzey, Paul D. Williams, Vinay Mittal, Daniel J. Mazur, Sihong Chen, Jason Wustman, Efren Ballesteros-Villagrana, Goutam Nistala, Santhoshi Bandla, Jim Veitch, Jon Sherlock, John Bishop, Seth Sadis. Analytical performance of a novel next generation sequencing assay for myeloid cancers [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 3863.

Binding MOAD (Mother of All Databases) is the largest collection of high-quality, protein-ligand complexes available from the Protein Data Bank. It has grown to 9837 hand-curated entries. Here, we describe our semi-annual updating procedures and BUDA (Binding Unstructured Data Analysis), a custom workflow tool that incorporates natural language processing technologies to facilitate the annotation process.

The lack of standardized methods for human phenotyping is a major obstacle in translational science. We have developed a bleeding history phenotyping system comprising an ontology, a questionnaire, a Web‐based phenotype recording instrument (PRI), and a database. The ontology facilitates transparency, collaboration, aggregation of data, and data analysis. The integrated system allows investigators worldwide to use the PRI, add their de‐identified data to the database, and query the aggregated data. Thus, this system can increase the power to detect genotype–phenotype–environment relationships and help new investigators begin their studies. We anticipate that this approach may be applicable to other disorders.

Through a prospective clinical sequencing program for advanced cancers, four index cases were identified which harbor gene rearrangements of FGFR2, including patients with cholangiocarcinoma, breast cancer, and prostate cancer. After extending our assessment of FGFR rearrangements across multiple tumor cohorts, we identified additional FGFR fusions with intact kinase domains in lung squamous cell cancer, bladder cancer, thyroid cancer, oral cancer, glioblastoma, and head and neck squamous cell cancer. All FGFR fusion partners tested exhibit oligomerization capability, suggesting a shared mode of kinase activation. Overexpression of FGFR fusion proteins induced cell proliferation. Two bladder cancer cell lines that harbor FGFR3 fusion proteins exhibited enhanced susceptibility to pharmacologic inhibition in vitro and in vivo. Because of the combinatorial possibilities of FGFR family fusion to a variety of oligomerization partners, clinical sequencing efforts, which incorporate transcriptome analysis for gene fusions, are poised to identify rare, targetable FGFR fusions across diverse cancer types. Significance: High-throughput sequencing technologies facilitate defining the mutational landscape of human cancers, which will lead to more precise treatment of patients with cancer. Here, through integrative sequencing efforts, we identified a variety of FGFR gene fusions in a spectrum of human cancers. FGFR fusions are active kinases. Cells harboring FGFR fusions showed enhanced sensitivity to the FGFR inhibitors PD173074 and pazopanib, suggesting that patients with cancer with FGFR fusions may benefit from targeted FGFR kinase inhibition. Cancer Discov; 3(6); 636–47. ©2013 AACR. See related commentary by Sabnis and Bivona, p. 607 This article is highlighted in the In This Issue feature, p. 591

The residue composition of a ligand binding site determines the interactions available for diffusion-mediated ligand binding, and understanding general composition of these sites is of great importance if we are to gain insight into the functional diversity of the proteome. Many structure-based drug design methods utilize such heuristic information for improving prediction or characterization of ligand-binding sites in proteins of unknown function. The Binding MOAD database if one of the largest curated sets of protein-ligand complexes, and provides a source of diverse, high-quality data for establishing general trends of residue composition from currently available protein structures. We present an analysis of 3,295 non-redundant proteins with 9,114 non-redundant binding sites to identify residues over-represented in binding regions versus the rest of the protein surface. The Binding MOAD database delineates biologically-relevant “valid” ligands from “invalid” small-molecule ligands bound to the protein. Invalids are present in the crystallization medium and serve no known biological function. Contacts are found to differ between these classes of ligands, indicating that residue composition of biologically relevant binding sites is distinct not only from the rest of the protein surface, but also from surface regions capable of opportunistic binding of non-functional small molecules. To confirm these trends, we perform a rigorous analysis of the variation of residue propensity with respect to the size of the dataset and the content bias inherent in structure sets obtained from a large protein structure database. The optimal size of the dataset for establishing general trends of residue propensities, as well as strategies for assessing the significance of such trends, are suggested for future studies of binding-site composition., Author Summary Describing the general structure of protein binding sites is fundamentally important for guiding drug design and better understanding structure-function relationships. Here, we analyze small molecules bound to proteins within our large database, Binding MOAD (Mother of All Databases, pronounced like “mode” as a pun referring to ligand-binding modes). We focus on different contacts across the residues in the binding sites, and we normalize the data relative to the protein's entire surface. A key feature of this study is the use of a “control” where we compare real, functional binding sites to the random contacts seen for crystallographic additives against the protein surface. Controls are required in experimental biology, but they are ill-defined in many computational approaches. This allows us to describe how true binding sites are unique on the protein surface and distinct from random patches that attract common, small molecules.

Summary. Background: Comparing a patient’s bleeding symptoms with those of healthy individuals is an important component of the diagnosis of bleeding disorders, but little is known about whether bleeding symptoms in healthy individuals vary by sex, race, ethnicity, age, or aspirin use. Objectives, Patients/Methods: We developed a comprehensive, ontology-backed, Web-based questionnaire to collect bleeding histories from 500 healthy adults. The mean age was 43 years (range 19–86 years), 63% were female, 19% were Hispanic, 37% were African-American, 43% were Caucasian, 8% were Asian, and 4% were multiracial. Results: 18 of the 36 symptoms captured occurred with

Physical differences in small molecule binding between enzymes and non-enzymes were found through mining the protein-ligand database, Binding MOAD (Mother of All Databases). The data suggest that divergent approaches may be more productive for improving the affinity of ligands for the two classes of proteins. High-affinity ligands of enzymes are much larger than those with low affinity, indicating that the addition of complementary functional groups is likely to improve the affinity of an enzyme inhibitor. However, this process may not be as fruitful for ligands of non-enzymes. High- and low-affinity ligands of non-enzymes are nearly the same size, so modest modifications and isosteric replacement might be most productive. The inherent differences between enzymes and non-enzymes have significant ramifications for scoring functions and structure-based drug design. In particular, non-enzymes were found to have greater ligand efficiencies than enzymes. Ligand efficiencies are often used to indicate druggability of a target, and this finding supports the feasibility of non-enzymes as drug targets. The differences in ligand efficiencies do not appear to come from the ligands; instead, the pockets yield different amino acid compositions, despite very similar distributions of amino acids in the overall protein sequences.

Patients with chromosomal rearrangements resulting in fusion proteins are amongst the most responsive to targeted therapy. For example, targeting of the BCR-ABL fusion in chronic myelogenous leukemia (CML) with imatinib and the EML4-ALK fusion in non-small cell lung cancer (NSCLC) with crizotinib has led to dramatic patient responses in these diseases. While crizotinib is approved for use in EML4-ALK positive NSCLC through its inhibition of ALK, the drug also inhibits ROS1, MST1R (RON), MET, and more recently has been shown to inhibit the ALK homolog, LTK. To gain a more comprehensive understanding of the full therapeutic potential of crizotinib, we undertook a genomic survey of ALK, LTK, ROS1, MET and MST1R across thousands of patients subjected to full exome sequencing including patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), as well as tens thousands of patients from Oncomine® databases. We confirmed the presence of EML4-ALK fusions in both lung and colorectal cancer (CRC), identified a PRKAR1A-ALK fusion in CRC, and found evidence of novel recurrent ALK fusions in kidney papillary renal cell carcinoma and thyroid gland carcinoma. ALK hotspot mutations and focal amplifications were confined to neuroblastoma, as previously described. We also report the first instance of an LTK fusion, identified in thyroid gland carcinoma. LTK amplifications were also observed in 1.4% of gastric cancers and rarely in medulloblastoma and breast cancer. LTK was prominently over-expressed in leukemia, and in an analysis of over 4,000 PML-RARA fusion positive leukemia patients, LTK was amongst the most significantly over-expressed genes. In addition to confirming previously published ROS1 fusions, our survey of ROS1 identified rare novel fusions in NSCLC and glioblastoma. High-level MET amplifications were observed in 1-5% of papillary renal cell carcinoma, the intestinal subtype of gastric adenocarcinoma, oligodendroglioma, glioblastoma and lung adenocarcinoma. Hotspot mutations in MET were frequently observed in head and neck squamous cell carcinoma (HNSCC) (11%), and observed in a third of metastatic HNSCC samples. Additional hotspot mutations were also observed in lung adenocarcinoma (2%) and small cell lung cancer (2%). Aberrations in MST1R were rare. These results leverage all available genomic profiling data to provide a broadened scope of therapeutic opportunity for inhibitors like crizotinib. With the growing availability of next-generation sequencing data and analyses, such surveys can support hypothesis-driven development of targeted therapies and help expand opportunities for clinical stage therapies. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C256. Citation Format: Sean Eddy, Mark Tomilo, Mary Ellen Urick, Nickolay A. Khazanov, Paul Williams, Armand Bankhead, Dinesh Cyanam, Supra Gajjala, Peter Wyngaard, Emma Bowden, Dan R. Rhodes. Expanded clinical opportunities for crizotinib from an analysis of over 5,000 cancer patient exomes. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C256.

The MAPK and PI3K pathways are frequently altered in human cancer and are targeted by dozens of agents in clinical trials. The successful application of these therapies, alone or in combination, may depend on the activation status of both pathways. Next-generation sequencing of cancer exomes provides a unique opportunity to systematically survey pathway alterations in cancer. Using somatic mutation data obtained from The Cancer Genome Atlas, we sought to catalog the members of the MAPK and PI3K pathways with driver mutations, the frequency of occurrence in common cancers and the frequency of co-occurrence. Furthermore, we sought to characterize the association of pathway mutation status with drug response in pre-clinical models. While the MAPK and PI3K pathways were frequently altered, the frequency of single and dual pathway alteration and the altered genes varied substantially across cancer types. The MAPK pathway was most frequently altered in rectal (62%), colon (59%), uterine (31%) and lung adenocarcinoma (45%) but infrequently altered in and breast cancer (4%). KRAS, BRAF and NRAS hotspot mutations were the most common pathway drivers, along with NF1 deleterious mutations in certain cancer types. The PI3K pathway was most frequently altered in uterine (84%), breast (40%) and glioblastoma (41%) but was rarely altered in lung adenocarcinoma (9%). Hotspot mutations in PIK3CA and hotspot and deleterious mutations in PTEN were the most common pathway alterations. In addition, predicted driver mutations occurred in PIK3R1, PIK3R3, MTOR, AKT1 and AKT3. Notably, MAPK and PI3K pathway alterations co-occurred in uterine (30%), colon (17%) and gastric (12%) cancers more so than would be expected by chance (p < 0.02). In contrast, other cancer types favored one pathway almost exclusively and thus had little co-occurrence. For example, breast cancer significantly favored PI3K pathway whereas lung adenocarcinoma favored MAPK pathway. To assess the effect of pathway mutation status on treatment response, we integrated hybrid-capture sequencing data from the Cancer Cell Line Encyclopedia with pharmacological data from over 150 compounds. We found that MAPK and PI3K pathway mutations most significantly associated with sensitivity to MEK and PI3K/AKT/mTOR inhibitors, respectively. Notably though, cell lines with co-occurring MAPK pathway and PIK3CA mutations were insensitive to MEK inhibitors and cell lines with co-occurring PI3K pathway and KRAS mutations were insensitive to PI3K inhibitors. Also, not all pathway mutations conferred equal sensitivity. For example, BRAF mutants were generally sensitive, KRAS mutants were mixed and NF1 mutants were generally insensitive to MEK inhibitors. Taken together, our work highlights the need to consider pathways and co-occurrence in the development of targeted therapies. Citation Format: Mark Tomilo, Paul D. Williams, Emma T. Bowden, Supra R. Gajjala, Santhoshi Bandla, Sean F. Eddy, Seth E. Sadis, Peter J. Wyngaard, Nickolay A. Khazanov, Daniel R. Rhodes. Discovery and characterization of driver MAPK and PI3K pathway mutations in tumors and association with drug response in cell lines. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2897. doi:10.1158/1538-7445.AM2013-2897

Background: Next generation sequence analysis of cancer exomes provides a comprehensive assessment of somatic mutations; however discerning often rare driver mutations from abundant passenger mutations remains a challenge. Unlike other approaches that rely on computational prediction of a single mutation’s function, we developed an approach that surveys a large collection of cancer exomes to identify genes with characteristic mutation patterns modeled on known oncogenes and tumor suppressor genes. We applied our approach to >2,700 cancer exomes to define the driver mutational landscape of common cancers. Materials and Methods: Somatic mutation data was obtained from The Cancer Genome Atlas. A total of 1,098,928 somatic mutations were integrated and re-annotated from 2,711 patients across 13 cancer types. After filtering for mutations within and in proximity to coding regions, 728,632 mutations remained. The proportion of recurrent hotspot and recurrent deleterious mutations were tabulated for each gene in each cancer type and then an oncogene vs. tumor suppressor vs. passenger classifier was trained on known cancer genes. Results: A median of 51 non-synonymous mutations per sample (range 1 to >20,000) was observed. Our classifier nominated 265 known or predicted oncogenes and 1019 known or predicted tumor suppressors, respectively, from a total of 20,451 genes. The vast majority of identified genes were mutated in small sub-populations of particular cancer types ( 10%), of which most are well-recognized oncogenes. Notably though, 52 additional nominated oncogenes were mutated in 5−10% of at least one cancer type, including functionally diverse genes whose role in cancer has not yet been well established (ANAPC1, NFE2L2, PPP2R1A, U2AF1) or were not previously known to be candidates for mutational activation (ERBB3, MAPK1). Among nominated tumor suppressor genes, 81 were frequently mutated in at least one cancer type (10%). In addition to many known tumor suppressors, other emerging cancer genes and gene families were found to be widely inactivated across many cancer types, including many genes involved in chromatin remodeling (ARID1A, PRBM1) and modification (MLL2, MLL3, MLL4, SETD2). Conclusions: Our analysis is the first of its kind to define the driver mutational landscape of common cancers based on classification of mutation patterns observed across thousands of exomes. We validated nearly all known driver cancer genes across 13 cancer types and nominated a wide spectrum of novel potential drivers, most of which were infrequently altered. Our work suggests several new potential therapeutic targets and identifies additional opportunities for existing targeted therapies.

This Erratum is to declare that the values reported for R2 in the paper are actually Pearson R values. The wrong column of data in a spreadsheet was used inadvertently. All correlation values in the paper are correct, just mislabeled with the squared superscript. One of the major conclusions noted in the abstract and discussed in the “Strengths and Weaknesses” Section should read: “Inherent experimental error limits the possible correlation between scores and measured affinity; Pearson R is limited to ∼0.91 (Pearson R2 ∼0.83) when fitting to the data set without over parameterizing. Pearson R is limited to ∼0.83 (Pearson R2 ∼0.70) when scoring the data set with a method trained on outside data.” For clarity, the Pearson R and R2 are given in Table ​Table11 below for all the theoretical cases posed. It corrects the correlation coefficients in Figure 3 and in the discussion of signal over noise in the “Strengths and Weaknesses” section. Table 1 Correlation Metrics when Random Error is Added to the 343 Affinity Data of the CSAR-NRC Data Seta It should be noted that our use of R2 is based on squaring the Pearson value, not based on a calculation of the coefficient of determination (also called R2). The coefficient of determination measures the one-to-one correspondence between two values, requiring a slope of 1 and an intercept at 0 rather than least-squares-fit values.