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.

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 GENIE, 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 deidentification 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 the treatment of cancer.

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.

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.

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.

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.

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.

Regulation of cardiovascular development and integrity by the heart of glass-cerebral cavernous malformation protein pathway.

Cerebral cavernous malformations (CCMs) are human vascular malformations caused by mutations in three genes of unknown function: KRIT1, CCM2 and PDCD10. Here we show that the heart of glass (HEG1) receptor, which in zebrafish has been linked to ccm gene function, is selectively expressed in endothelial cells. Heg1(-/-) mice showed defective integrity of the heart, blood vessels and lymphatic vessels. Heg1(-/-); Ccm2(lacZ/+) and Ccm2(lacZ/lacZ) mice had more severe cardiovascular defects and died early in development owing to a failure of nascent endothelial cells to associate into patent vessels. This endothelial cell phenotype was shared by zebrafish embryos deficient in heg, krit1 or ccm2 and reproduced in CCM2-deficient human endothelial cells in vitro. Defects in the hearts of zebrafish lacking heg or ccm2, in the aortas of early mouse embryos lacking CCM2 and in the lymphatic vessels of neonatal mice lacking HEG1 were associated with abnormal endothelial cell junctions like those observed in human CCMs. Biochemical and cellular imaging analyses identified a cell-autonomous pathway in which the HEG1 receptor couples to KRIT1 at these cell junctions. This study identifies HEG1-CCM protein signaling as a crucial regulator of heart and vessel formation and integrity.

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.

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.

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.