Biomarker Inference and the Timing of Next-Generation Sequencing in a Multi-Institutional, Cross-Cancer Clinicogenomic Data Set

PURPOSE

Observational clinicogenomic data sets, consisting of tumor next-generation sequencing (NGS) data linked to clinical records, are commonly used for cancer research. However, in real-world practice, oncologists frequently request NGS in search of treatment options for progressive cancer. The extent and impact of this dynamic on analysis of clinicogenomic research data are not well understood.

METHODS

We analyzed clinicogenomic data for patients with non-small cell lung, colorectal, breast, prostate, pancreatic, or urothelial cancers in the American Association for Cancer Research Biopharmaceutical Consortium cohort. Associations between baseline and time-varying clinical characteristics and time from diagnosis to NGS were measured. To explore the impact of informative cohort entry on biomarker inference, statistical interactions between selected biomarkers and time to NGS with respect to overall survival were calculated.

RESULTS

Among 7,182 patients, time from diagnosis to NGS varied significantly by clinical factors, including cancer type, calendar year of sequencing, institution, and age and stage at diagnosis. NGS rates also varied significantly by dynamic clinical status variables; in an adjusted model, compared with patients with stable disease at any given time after diagnosis, patients with progressive disease by imaging or oncologist assessment had higher NGS rates (hazard ratio for NGS, 1.61 [95% CI, 1.45 to 1.78] and 2.32 [95% CI, 2.01 to 2.67], respectively). Statistical interactions between selected biomarkers and time to NGS with respect to survival, potentially indicating biased biomarker inference results, were explored.

CONCLUSION

To evaluate the appropriateness of a data set for a particular research question, it is crucial to measure associations between dynamic cancer status and the timing of NGS, as well as to evaluate interactions involving biomarkers of interest and NGS timing with respect to survival outcomes.

Genomic Characterization and Clinical Outcomes of Patients with Peritoneal Metastases from the AACR GENIE Biopharma Collaborative Colorectal Cancer Registry

Peritoneal metastases (PM) are common in metastatic colorectal cancer (mCRC). We aimed to characterize patients with mCRC and PM from a clinical and molecular perspective using the American Association of Cancer Research Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC) registry. Patients' tumor samples underwent targeted next-generation sequencing. Clinical characteristics and treatment outcomes were collected retrospectively. Overall survival (OS) from advanced disease and progression-free survival (PFS) from start of cancer-directed drug regimen were estimated and adjusted for the left truncation bias. A total of 1,281 patients were analyzed, 244 (19%) had PM at time of advanced disease. PM were associated with female sex [OR: 1.67; 95% confidence interval (CI): 1.11-2.54; P = 0.014] and higher histologic grade (OR: 1.72; 95% CI: 1.08-2.71; P = 0.022), while rectal primary tumors were less frequent in patients with PM (OR: 0.51; 95% CI: 0.29-0.88; P < 0.001). APC occurred less frequently in patients with PM (N = 151, 64% vs. N = 788, 79%) while MED12 alterations occurred more frequently in patients with PM (N = 20, 10% vs. N = 32, 4%); differences in MED12 were not significant when restricting to oncogenic and likely oncogenic variants according to OncoKB. Patients with PM had worse OS (HR: 1.45; 95% CI: 1.16-1.81) after adjustment for independently significant clinical and genomic predictors. PFS from initiation of first-line treatment did not differ by presence of PM. In conclusion, PM were more frequent in females and right-sided primary tumors. Differences in frequencies of MED12 and APC alterations were identified between patients with and without PM. PM were associated with shorter OS but not with PFS from first-line treatment.

SIGNIFICANCE

Utilizing the GENIE BPC registry, this study found that PM in patients with colorectal cancer occur more frequently in females and right-sided primary tumors and are associated with worse OS. In addition, we found a lower frequency of APC alterations and a higher frequency in MED12 alterations in patients with PM.

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal

International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer.

SIGNIFICANCE

Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

The GENIE BPC NSCLC Cohort: A Real-World Repository Integrating Standardized Clinical and Genomic Data for 1,846 Patients with Non-Small Cell Lung Cancer

PURPOSE

We describe the clinical and genomic landscape of the non-small cell lung cancer (NSCLC) cohort of the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC).

EXPERIMENTAL DESIGN

A total of 1,846 patients with NSCLC whose tumors were sequenced from 2014 to 2018 at four institutions participating in AACR GENIE were randomly chosen for curation using the PRISSMM data model. Progression-free survival (PFS) and overall survival (OS) were estimated for patients treated with standard therapies.

RESULTS

In this cohort, 44% of tumors harbored a targetable oncogenic alteration, with EGFR (20%), KRAS G12C (13%), and oncogenic fusions (ALK, RET, and ROS1; 5%) as the most frequent. Median OS (mOS) on first-line platinum-based therapy without immunotherapy was 17.4 months [95% confidence interval (CI), 14.9-19.5 months]. For second-line therapies, mOS was 9.2 months (95% CI, 7.5-11.3 months) for immune checkpoint inhibitors (ICI) and 6.4 months (95% CI, 5.1-8.1 months) for docetaxel ± ramucirumab. In a subset of patients treated with ICI in the second-line or later setting, median RECIST PFS (2.5 months; 95% CI, 2.2-2.8) and median real-world PFS based on imaging reports (2.2 months; 95% CI, 1.7-2.6) were similar. In exploratory analysis of the impact of tumor mutational burden (TMB) on survival on ICI treatment in the second-line or higher setting, TMB z-score harmonized across gene panels was associated with improved OS (univariable HR, 0.85; P = 0.03; n = 247 patients).

CONCLUSIONS

The GENIE BPC cohort provides comprehensive clinicogenomic data for patients with NSCLC, which can improve understanding of real-world patient outcomes.

Challenges to Using Big Data in Cancer

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case studies for overcoming challenges in using big data in cancer

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data de-identification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and treatment of cancer.

AACR Project GENIE: 100,000 Cases and Beyond

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from the United States, Canada, the United Kingdom, France, the Netherlands, and Spain. Here, we demonstrate the use of these real-world data, harmonized through a centralized data resource, to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharmaceutical collaborations and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledge base of increasing impact to inform clinical decision-making and bring together cancer researchers internationally.

SIGNIFICANCE

AACR Project GENIE has now accrued data from >110,000 tumors, placing it among the largest repository of publicly available, clinically annotated genomic data in the world. GENIE has emerged as a powerful resource to evaluate genome-guided clinical trial design, uncover drivers of cancer subtypes, and inform real-world use of genomic data. This article is highlighted in the In This Issue feature, p. 2007.

Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies

Importance

Real-world data sets that combine clinical and genomic data may be subject to left truncation (when potential study participants are not included because they have already passed the milestone of interest at the time of study recruitment). The lapse between diagnosis and molecular testing can present analytic challenges and threaten the validity and interpretation of survival analyses.

Observations

Effects of ignoring left truncation when estimating overall survival are illustrated using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC), and a straightforward risk-set adjustment approach is described. Ignoring left truncation results in overestimation of overall survival: unadjusted median survival estimates from diagnosis among patients with stage IV non-small cell lung cancer or stage IV colorectal cancer were overestimated by more than 1 year.

Conclusions and Relevance

Clinicogenomic data are a valuable resource for evaluation of real-world cancer outcomes and should be analyzed using appropriate methods to maximize their potential. Analysts must become adept at application of appropriate statistical methods to ensure valid, meaningful, and generalizable research findings.

Correlation Between Surrogate End Points and Overall Survival in a Multi-institutional Clinicogenomic Cohort of Patients With Non-Small Cell Lung or Colorectal Cancer

IMPORTANCE

Contemporary observational cancer research requires associating genomic biomarkers with reproducible end points; overall survival (OS) is a key end point, but interpretation can be challenging when multiple lines of therapy and prolonged survival are common. Progression-free survival (PFS), time to treatment discontinuation (TTD), and time to next treatment (TTNT) are alternative end points, but their utility as surrogates for OS in real-world clinicogenomic data sets has not been well characterized.

OBJECTIVE

To measure correlations between candidate surrogate end points and OS in a multi-institutional clinicogenomic data set.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study was conducted of patients with non-small cell lung cancer (NSCLC) or colorectal cancer (CRC) whose tumors were genotyped at 4 academic centers from January 1, 2014, to December 31, 2017, and who initiated systemic therapy for advanced disease. Patients were followed up through August 31, 2020 (NSCLC), and October 31, 2020 (CRC). Statistical analyses were conducted on January 5, 2021.

EXPOSURES

Candidate surrogate end points included TTD; TTNT; PFS based on imaging reports only; PFS based on medical oncologist ascertainment only; PFS based on either imaging or medical oncologist ascertainment, whichever came first; and PFS defined by a requirement that both imaging and medical oncologist ascertainment have indicated progression.

MAIN OUTCOMES AND MEASURES

The primary outcome was the correlation between candidate surrogate end points and OS.

RESULTS

There were 1161 patients with NSCLC (648 women [55.8%]; mean [SD] age, 63 [11] years) and 1150 with CRC (647 men [56.3%]; mean [SD] age, 54 [12] years) identified for analysis. Progression-free survival based on both imaging and medical oncologist documentation was most correlated with OS (NSCLC: ρ = 0.76; 95% CI, 0.73-0.79; CRC: ρ = 0.73; 95% CI, 0.69-0.75). Time to treatment discontinuation was least associated with OS (NSCLC: ρ = 0.45; 95% CI, 0.40-0.50; CRC: ρ = 0.13; 95% CI, 0.06-0.19). Time to next treatment was modestly associated with OS (NSCLC: ρ = 0.60; 0.55-0.64; CRC: ρ = 0.39; 95% CI, 0.32-0.46).

CONCLUSIONS AND RELEVANCE

This cohort study suggests that PFS based on both a radiologist and a treating oncologist determining that a progression event has occurred was the surrogate end point most highly correlated with OS for analysis of observational clinicogenomic data.

Abstract 2620: Ignoring left truncation in overall survival within real-world genomic-phenomic data leads to inflated survival estimates

Studies linking genomic and phenomic data are subject to selection biases, including delayed entry or immortal time bias. Delayed entry can be problematic for time-to-event analyses, but utilization of appropriate statistical methods to account for delayed entry are underutilized. Delayed entry commonly occurs when genomic sequencing results are obtained after the start time for survival estimation.

To evaluate the impact of left truncation on overall survival (OS) estimates, we explored outcomes in patients with de novo stage IV non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) from the AACR GENIE Biopharma Collaborative, who had genomic sequencing within a specified timeframe. We analyzed OS from diagnosis and from start of the most common first-line regimen, carboplatin/pemetrexed for NSCLC (N = 212 patients) and FOLFOX for CRC (N = 369 patients). We compared median OS using standard Kaplan-Meier methods to median OS using left truncation methods to account for delayed entry. All NSCLC and CRC patients underwent genomic sequencing after their diagnosis date. Among NSCLC patients on carboplatin/pemetrexed, 41% and among CRC patients on FOLFOX, 14% had sequencing determined after starting first-line regimen. The survfit function in R package survival was used, and the absolute differences and percent differences in median OS estimates were calculated.

Failure to account for delayed entry leads to an overestimation of OS, regardless of cohort and start date. Adjusting survival outcomes using left truncation methods reduces the influence of some aspects of selection bias and results in better estimates of time to event outcomes. Analyses from these cohorts can provide meaningful insights about survival outcomes outside the clinical trial setting and may support trial design and reliable selection of control arms. As such, it is imperative that analytic methods to account for the inflated survival estimates are incorporated.

EstimateCRC Stage IV (N = 658)NSCLC Stage IV (N = 722)Unadjusted Median (IQR) Overall Survival from Diagnosis (Years)3.2 (2.9, 3.4)2.3 (2.0, 2.5)Median (IQR) Overall Survival from Diagnosis in Years, Adjusting for Delayed Entry2.1 (1.9, 2.4)1.3 (1.1, 1.6)Difference in Medians (Years)1.11.0% Difference in Medians34%44%EstimateCRC Stage IV (N = 369)NSCLC Stage IV (N = 212)Unadjusted Median (IQR) Overall Survival from Most Common First-Line Regimen (Years)2.9 (2.6, 3.4)1.3 (1.0, 1.6)Median (IQR) Overall Survival from Most Common First-Line Regimen in Years, Adjusting for Delayed Entry2.1 (1.8, 2.5)0.9 (0.7, 1.2)Difference in Medians (Years)0.80.4% Difference in Medians28%31%

Citation Format: Samantha Brown, Jessica A. Lavery, Eva M. Lepisto, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Ritika Kundra, Brooke Mastrogiacomo, Phillipe Bedard, Jeremy L. Warner, Gregory J. Riely, Deborah Schrag, Katherine S. Panageas, The AACR Project GENIE Consortium. Ignoring left truncation in overall survival within real-world genomic-phenomic data leads to inflated survival estimates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2620.

Abstract 2619: Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data

Obtaining information regarding cancer recurrence from a retrospective, EHR-based dataset poses several challenges primarily due to the lack of structured data. Patients are at risk for cancer recurrence beginning at a time point at which they are characterized as having no evidence of disease. The absence of cancer may be indicated on a radiology report or a medical oncologist assessment, requiring manual review and interpretation of potentially ambiguous free text. Further, the recurrence event itself can be defined based on several distinct data sources including pathology, imaging, clinician assessments, or tumor markers. The likelihood of ascertaining recurrence is dependent on the frequency and type of surveillance performed and varies based on tumor type and based on clinicians' thresholds for pursuing workup of borderline or suspicious findings; if follow up assessments are infrequent, there are fewer opportunities to detect recurrence. Given these challenges, there is currently no standardized approach to evaluating cancer recurrence in EHR data, impeding analyses of rare molecular tumor subtypes in multi-institutional linked clinico-genomic databases.

For this analysis, we leveraged the AACR Project GENIE Biopharma Collaborative data based on the PRISSMM curation model to develop an algorithm for identifying recurrence among patients diagnosed with stage I-III non-small cell lung cancer or with stage I-III colorectal cancer. This algorithm involves using curated pathology report data to identify a definitive surgery as the time at which patients have completed curative intent treatment. Subsequent imaging reports, pathology reports, medical oncologist assessments and tumor marker data are then evaluated in order to characterize the timing of specific recurrence events.

We will present the real-world recurrence algorithm, its underlying rationale and discuss applications of recurrence endpoints. Beyond enabling estimates of recurrence-free survival, identifying cancer recurrence will allow for estimation of progression-free survival among stage I-III patients in addition to estimation of PFS among de novo stage IV patients. Estimating PFS in a large cohort of patients with linked phenomic and genomic data has historically been a limitation of these types of datasets. Overcoming this limitation will allow for precision medicine advances in oncology by facilitating data pooling across institutions and enabling examination of rare molecular subtypes in relation to clinically meaningful endpoints.

Citation Format: Jessica A. Lavery, Samantha Brown, Eva Lepisto, Michele L. Lenoue-Newton, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Brooke Mastrogiacomo, Ritika Kundra, Jeremy Warner, Philippe Bedard, Gregory J. Riely, Katherine S. Panageas, Deborah Schrag, AACR Project GENIE Consortium. Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2619.

Characteristics and Outcome of AKT1 E17K-Mutant Breast Cancer Defined through AACR Project GENIE, a Clinicogenomic Registry

AKT inhibitors have promising activity in AKT1 ^E17K-mutant estrogen receptor (ER)-positive metastatic breast cancer, but the natural history of this rare genomic subtype remains unknown. Utilizing AACR Project GENIE, an international clinicogenomic data-sharing consortium, we conducted a comparative analysis of clinical outcomes of patients with matched AKT1 ^E17K-mutant (n = 153) and AKT1-wild-type (n = 302) metastatic breast cancer. AKT1-mutant cases had similar adjusted overall survival (OS) compared with AKT1-wild-type controls (median OS, 24.1 vs. 29.9, respectively; P = 0.98). AKT1-mutant cases enjoyed longer durations on mTOR inhibitor therapy, an observation previously unrecognized in pivotal clinical trials due to the rarity of this alteration. Other baseline clinicopathologic features, as well as durations on other classes of therapy, were broadly similar. In summary, we demonstrate the feasibility of using a novel and publicly accessible clincogenomic registry to define outcomes in a rare genomically defined cancer subtype, an approach with broad applicability to precision oncology. SIGNIFICANCE: We delineate the natural history of a rare genomically distinct cancer, AKT1 ^E17K-mutant ER-positive breast cancer, using a publicly accessible registry of real-world patient data, thereby illustrating the potential to inform drug registration through synthetic control data.See related commentary by Castellanos and Baxi, p. 490.

American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange: From Inception to First Data Release and Beyond-Lessons Learned and Member Institutions' Perspectives

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international data-sharing consortium focused on enabling advances in precision oncology through the gathering and sharing of tumor genetic sequencing data linked with clinical data. The project's history, operational structure, lessons learned, and institutional perspectives on participation in the data-sharing consortium are reviewed. Individuals involved with the inception and execution of AACR Project GENIE from each member institution described their experiences and lessons learned. The consortium was conceived in January 2014 and publicly released its first data set in January 2017, which consisted of 18,804 samples from 18,324 patients contributed by the eight founding institutions. Commitment and contributions from many individuals at AACR and the member institutions were crucial to the consortium's success. These individuals filled leadership, project management, informatics, data curation, contracts, ethics, and security roles. Many lessons were learned during the first 3 years of the consortium, including on how to gather, harmonize, and share data; how to make decisions and foster collaboration; and how to set the stage for continued participation and expansion of the consortium. We hope that the lessons shared here will assist new GENIE members as well as others who embark on the journey of forming a genomic data-sharing consortium.

Abstract LB-102: Landscape analysis of the initial data release from AACR Project GENIE

AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) is a multi-phase, multi-year, international data-sharing consortium whose goal is to generate an evidence base for precision cancer medicine by integrating and linking clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. The project fulfills an unmet need in oncology by providing the statistical power necessary to identify novel therapeutic targets, to understand genomic determinants of response to therapy, to design new biomarker-driven clinical trials and ultimately, to improve clinical decision-making and the care delivered to patients. Here we describe the goals, structure and data standards of the GENIE consortium and conclusions from a high-level analysis of the first public release of genomic and limited clinical data from approximately 19,000 patients treated at eight cancer centers obtained during this initial phase of the project. We also explore the clinical utility of these genomic data by examining rates of clinical actionability across multiple cancer types and by estimating patient enrollment rates to the NCI MATCH Trial. Based on yearly rates of sequencing at each of the eight founding institutions, together with the planned addition of new members, we estimate the GENIE database could grow to &gt;100,000 samples within five years. Consistent with the goals of the proposed Cancer Moonshot National Cancer Data Ecosystem, GENIE is committed to the principles of generating interoperable, open access data that can be widely shared across the entire scientific community.

Citation Format: Ethan Cerami, Alexander S. Baras, Justin Guinney, Eva Lepisto, Trevor J. Pugh, Nikolaus Schultz, Thomas Stricker, Shawn M. Sweeney, Laura J. van't Veer, Gerrit A. Meijer, Fabrice Andre, Victor E. Velculescu, Kenna R. Shaw, Mia A. Levy, Philippe L. Bedard, Barrett J. Rollins, Charles L. Sawyers, on behalf of the AACR Project GENIE Consortium. Landscape analysis of the initial data release from AACR Project GENIE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-102. doi:10.1158/1538-7445.AM2017-LB-102

Candidate cell and matrix interaction domains on the collagen fibril, the predominant protein of vertebrates

Type I collagen, the predominant protein of vertebrates, polymerizes with type III and V collagens and non-collagenous molecules into large cable-like fibrils, yet how the fibril interacts with cells and other binding partners remains poorly understood. To help reveal insights into the collagen structure-function relationship, a data base was assembled including hundreds of type I collagen ligand binding sites and mutations on a two-dimensional model of the fibril. Visual examination of the distribution of functional sites, and statistical analysis of mutation distributions on the fibril suggest it is organized into two domains. The "cell interaction domain" is proposed to regulate dynamic aspects of collagen biology, including integrin-mediated cell interactions and fibril remodeling. The "matrix interaction domain" may assume a structural role, mediating collagen cross-linking, proteoglycan interactions, and tissue mineralization. Molecular modeling was used to superimpose the positions of functional sites and mutations from the two-dimensional fibril map onto a three-dimensional x-ray diffraction structure of the collagen microfibril in situ, indicating the existence of domains in the native fibril. Sequence searches revealed that major fibril domain elements are conserved in type I collagens through evolution and in the type II/XI collagen fibril predominant in cartilage. Moreover, the fibril domain model provides potential insights into the genotype-phenotype relationship for several classes of human connective tissue diseases, mechanisms of integrin clustering by fibrils, the polarity of fibril assembly, heterotypic fibril function, and connective tissue pathology in diabetes and aging.

Angiogenesis in collagen I requires alpha2beta1 ligation of a GFP*GER sequence and possibly p38 MAPK activation and focal adhesion disassembly

Angiogenesis depends on proper collagen biosynthesis and cross-linking, and type I collagen is an ideal angiogenic scaffold, although its mechanism is unknown. We examined angiogenesis using an assay wherein confluent monolayers of human umbilical vein endothelial cells were overlain with collagen in a serum-free defined medium. Small spaces formed in the cell layer by 2 h, and cells formed net-like arrays by 6-8 h and capillary-like lumens by 24 h. Blocking of alpha2beta1, but not alpha1 or alpha(v)beta3 integrin function halted morphogenesis. We found that a triple-helical, homotrimeric peptide mimetic of a putative alpha2beta1 binding site: alpha1(I)496-507 GARGERGFP*GER (where single-letter amino acid nomenclature is used, P* = hydroxyproline) inhibited tube formation, whereas a peptide carrying another putative site: alpha1(I)127-138 GLP*GERGRP*GAP* or control peptides did not. A chemical inhibitor of p38 mitogen-activated protein kinase (p38 MAPK), SB202190, blocked tube formation, and p38 MAPK activity was increased in collagen-treated cultures, whereas targeting MAPK kinase (MEK), focal adhesion kinase (FAK), or phosphatidylinositol 3-kinase (PI3K) had little effect. Collagen-treated cells had fewer focal adhesions and 3- to 5-fold less activated FAK. Thus capillary morphogenesis requires endothelial alpha2beta1 integrin engagement of a single type I collagen integrin-binding site, possibly signaling via p38 MAPK and focal adhesion disassembly/FAK inactivation.

Endorepellin, a novel inhibitor of angiogenesis derived from the C terminus of perlecan

Perlecan, a ubiquitous basement membrane heparan sulfate proteoglycan, plays key roles in blood vessel growth and structural integrity. We discovered that the C terminus of perlecan potently inhibited four aspects of angiogenesis: endothelial cell migration, collagen-induced endothelial tube morphogenesis, and blood vessel growth in the chorioallantoic membrane and in Matrigel plug assays. The C terminus of perlecan was active at nanomolar concentrations and blocked endothelial cell adhesion to fibronectin and type I collagen, without directly binding to either protein; henceforth we have named it "endorepellin." We also found that endothelial cells possess a significant number of high affinity (K(d) of 11 nm) binding sites for endorepellin and that endorepellin binds endostatin and counteracts its anti-angiogenic effects. Thus, endorepellin represents a novel anti-angiogenic product, which may retard tumor neovascularization and hence tumor growth in vivo.

Mapping the ligand-binding sites and disease-associated mutations on the most abundant protein in the human, type I collagen

Type I collagen is the most abundant protein in humans, and it helps to maintain the integrity of many tissues via its interactions with cell surfaces, other extracellular matrix molecules, and growth and differentiation factors. Nearly 50 molecules have been found to interact with type I collagen, and for about half of them, binding sites on this collagen have been elucidated. In addition, over 300 mutations in type I collagen associated with human connective tissue disorders have been described. However, the spatial relationships between the known ligand-binding sites and mutation positions have not been examined. To this end, here we have created a map of type I collagen that includes all of its ligand-binding sites and mutations. The map reveals the existence of several hot spots for ligand interactions on type I collagen and that most of the binding sites locate to its C-terminal half. Moreover, on the collagen fibril some potentially relevant relationships between binding sites were observed including the following: fibronectin- and certain integrin-binding regions are near neighbors, which may mechanistically relate to fibronectin-dependent cell-collagen attachment; proteoglycan binding may potentially impact upon collagen fibrillogenesis, cell-collagen attachment, and collagen glycation seen in diabetes and aging; and mutations associated with osteogenesis imperfecta and other disorders show apparently nonrandom distribution patterns within both the monomer and fibril, implying that mutation positions correlate with disease phenotype. These and other observations presented here may provide novel insights into evaluating type I collagen functions and the relationships between its binding partners and mutations.

Versatility of BCR/ABL-expressing leukemic cells in circumventing proapoptotic BAD effects

BAD, the proapoptotic member of the "BH3-only" subfamily of BCL-2 proteins, is inactivated by phosphorylation at serines 112 and 136 and by sequestration in the cytoplasm where it interacts with members of the 14-3-3 family. In BCR/ABL-expressing cells, BAD is constitutively phosphorylated and mainly cytoplasmic, whereas in cells expressing BCR/ABL mutants unable to protect from apoptosis, BAD is nonphosphorylated. We show here that both the wild-type (WT) and the S112A/ S136A double mutant (DM) BAD are more potent inducers of apoptosis in parental than in BCR/ABL-expressing 32D myeloid precursor cells. Stable lines of parental cells expressing DM BAD could not be established and most clones from WT BAD retrovirus-infected parental cells lost BAD expression. On IL-3 withdrawal from parental 32D cells, BAD was rapidly dephosphorylated by the serine-threonine phosphatase 1 alpha, and localized in the mitochondria, whereas it remained phosphorylated and did not localize to the mitochondria in the cohort of BCR/ABL-expressing cells escaping apoptosis induced by WT BAD. Moreover, these cells showed high levels of BCL-2 and BCL-X(L) expression. The cohort of BCR/ABL-expressing cells resistant to apoptosis induced by DM BAD showed only high levels of BCL-2 and BCL-X(L). These findings suggest that BCR/ABL-expressing cells are more versatile than normal hematopoietic progenitors in counteracting the apoptotic potential of BAD, and raise the possibility that tumor cells activate multiple antiapoptotic pathways for survival in the face of death-inducing stimuli. (Blood. 2000;96:676-684)

Defining the domains of type I collagen involved in heparin- binding and endothelial tube formation

Cell surface heparan sulfate proteoglycan (HSPG) interactions with type I collagen may be a ubiquitous cell adhesion mechanism. However, the HSPG binding sites on type I collagen are unknown. Previously we mapped heparin binding to the vicinity of the type I collagen N terminus by electron microscopy. The present study has identified type I collagen sequences used for heparin binding and endothelial cell-collagen interactions. Using affinity coelectrophoresis, we found heparin to bind as follows: to type I collagen with high affinity (Kd approximately 150 nM); triple-helical peptides (THPs) including the basic N-terminal sequence alpha1(I)87-92, KGHRGF, with intermediate affinities (Kd approximately 2 microM); and THPs including other collagenous sequences, or single-stranded sequences, negligibly (Kd >> 10 microM). Thus, heparin-type I collagen binding likely relies on an N-terminal basic triple-helical domain represented once within each monomer, and at multiple sites within fibrils. We next defined the features of type I collagen necessary for angiogenesis in a system in which type I collagen and heparin rapidly induce endothelial tube formation in vitro. When peptides, denatured or monomeric type I collagen, or type V collagen was substituted for type I collagen, no tubes formed. However, when peptides and type I collagen were tested together, only the most heparin-avid THPs inhibited tube formation, likely by influencing cell interactions with collagen-heparin complexes. Thus, induction of endothelial tube morphogenesis by type I collagen may depend upon its triple-helical and fibrillar conformations and on the N-terminal heparin-binding site identified here.

Effective targeting of magnetic radioactive 90Y-microspheres to tumor cells by an externally applied magnetic field. Preliminary in vitro and in vivo results

Magnetic biodegradable poly(lactic acid) microspheres that incorporate both magnetite and the beta-emitter 90Y were prepared. By applying a directional external magnetic field gradient in excess of 0.02 Tesla/cm across a 96-well plate containing neuroblastoma cells incubated with the 90Y magnetite loaded microspheres, the radiation dose to the cells could be enhanced or reduced relative to the dose from a uniform loading of the well with 90Y-DTPA. Using the MTT assay, cell survival was measured for the magnetic field directed from above (cell sparing) and from below (cell targeting) the well plate, resulting in 65 +/- 8% or 18 +/- 5% survival respectively. This method was then applied to an in vivo murine tumor model. The biodistribution of intraperitoneally injected magnetic radioactive microspheres, after 24 h in mice, showed that 73 +/- 32% of the radioactivity was found on the subcutaneous tumor that had a rare earth magnet fixed above it. In contrast, the tumor radioactivity with no attached magnet was 6 +/- 4%. Magnetically targeted radiopolymers such as 90Y-microspheres show great promise for regional or intracavitary radiotherapy.

Magnetically directed poly(lactic acid) 90Y-microspheres: novel agents for targeted intracavitary radiotherapy

High energy beta-emitting radioisotopes like Yttrium-90 have a radiotoxic range of about one centimeter. For cancer treatment they must be brought near the tumor cells and kept there for as long as they are radioactive. We developed as carriers for the ionic form of 90Y a matrix-type polymeric drug delivery system, poly(lactic acid) (PLA) microspheres. This radiopharmaceutical could be selectively delivered to the target site after incorporating 10% Fe3O4 (magnetite) which made the magnetic microspheres (MMS) responsive to an external magnetic field. Furthermore, MMS are biodegradable and slowly hydrolyze into physiologic lactic acid after the radioactivity is completely decayed. Previously prepared 10-40 microns MMS were radiochemically loaded to high specific activity with 90Y at a pH of 5.7. Stability studies showed that approximately 95% of added 90Y is retained within the PLA matrix after 28 days (> 10 half-lives) at 37 degrees C in serum, and electron microscopy showed that the microspheres retained their characteristic morphologic appearance for the same time period. Cytotoxicity studies with SK-N-SH neuroblastoma cells growing in monolayer showed that the radiocytotoxicity of the microspheres could be directed magnetically to either kill or spare specific cell populations, thus making them of great interest for targeted intracavitary tumor therapy. We are currently optimizing this system for use in the treatment of neoplastic meningitis.

Cotransport of sodium and chloride by the adult mammalian choroid plexus

Cerebrospinal fluid formation stems primarily from the transport of Na and Cl in choroid plexus (CP). To characterize properties and modulation of choroidal transporters, we tested diuretics and other agents for ability to alter ion transport in vitro. Adult Sprague-Dawley rats were the source of CPs preincubated with drug for 20 min and then transferred to cerebrospinal fluid (CSF) medium containing 22Na or 36Cl with [3H]mannitol (extracellular correction). Complete base-line curves were established for cellular uptake of Na and Cl at 37 degrees C. The half-maximal uptake occurred at 12 s, so it was used to assess drug effects on rate of transport (nmol Na or Cl/mg CP). Bumetanide (10(-5) and 10(-4) M) decreased uptake of Na and Cl with maximal inhibition (up to 45%) at 10(-5) M. Another cotransport inhibitor, furosemide (10(-4) M), reduced transport of Na by 25% and Cl by 33%. However, acetazolamide (10(-4) M) and atriopeptin III (10(-7) M) significantly lowered uptake of Na (but not Cl), suggesting effect(s) other than on cotransport. The disulfonic stilbene 4,4'-diisothiocyanostilbene-2,2'-disulfonic acid (DIDS; 10(-4) M), known to inhibit Cl-HCO3 exchange, substantially reduced the transport of 36Cl. Bumetanide plus DIDS (both 10(-4) M) caused additive inhibition of 90% of Cl uptake, which provides strong evidence for the existence of both cotransport and antiport Cl carriers. Overall, this in vitro analysis, uncomplicated by variables of blood flow and neural tone, indicates the presence in rat CP of the cotransport of Na and Cl in addition to the established Na-H and Cl-HCO3 exchangers.