Abstract 1953: Accelerating prediction of pediatric and rare cancer vulnerabilities using next-generation cancer models

Ongoing pre-clinical efforts aim to deploy genome-scale CRISPR/Cas9 technology and large collections of small molecules to catalog maps of cancer vulnerabilities at scale. However, such efforts in pediatric and rare cancers have lagged behind comparable efforts in more common cancer types due to the dearth of cell models. Here, we present an update from our “Cancer Cell Line Factory” project on efforts to overcome key laboratory and biologistics challenges precluding progress in pediatric and rare cancers. This effort, now in it’s 3rd year, represents an industry scale pipeline aiming to generate, characterize and share novel cancer models of many tumor types with the scientific community. Overall, we have processed 1153 samples from 818 patients across over 16 cancer types through this pipeline with a 28% success rate overall, including over 350 patient samples from rare and pediatric cancers. To optimize conditions for each tumor type, we have systematically compared published methods including (1) next-generation 2-dimension, (2) organoid and (3) standard approaches and have captured all information with a data management system that should enhance the ability to predict optimal ex vivo propagation conditions for future samples. Among the successful cell models verified already as part of this effort, we have generated a series of over 30 unique pediatric and rare cancer models, many of which represent the first of their kind. We screened these and other models against a library of highly annotated 440 small molecules that were previously tested against 860 existing cancer cell lines. Our results suggest that dependency data generated with novel next-generation cell cultures is potentially backwards-compatible with existing small molecule dependency datasets. Furthermore, we tested the novel Broad Institute Drug Repurposing library consisting of 4100 approved therapeutics, or those under investigation for any disease, against the first cell line models of several of these rare next generation models including angioimmunoblastic T-cell lymphoma and renal medullary carcinoma, leading to several novel drug repurposing hypotheses for rare cancers. Given these proof-of-concept studies, in partnership with the Rare Cancer Research Foundation, we launched an online matchmaking platform to connect patients with rare cancers to available research studies, facilitate online consent and provide biologistics support to enable fresh tissue donation to support cancer model generation from any clinical site in the United States. We will present results from this novel direct-to-patient approach to facilitate the generation of even larger numbers of next generation models from rare and pediatric cancers, propelling the generation of pre-clinical dependency maps of these tumors for the scientific community.

Citation Format: Yuen-Yi Tseng, Andrew Hong, Paula Keskula, Shubhroz Gill, Jaime Cheah, Grigoriy Kryukov, Aviad Tsherniak, Francisca Vazquez, Glenn Cowley, Sahar Alkhairy, Coyin Oh, Anson Peng, Rebecca Deasy, Abeer Sayeed, Peter Ronning, Samuel Ng, Steven Corsello, Corrie Painter, David Sandak, Levi Garraway, Mark Rubin, Calvin Kuo, Sidharth Puram, David Weinstock, Adam Bass, Nikhil Wagle, Keith Ligon, Katherine Janeway, David Root, Stuart Schreiber, Paul Clemons, Aly Shamji, Aly Shamji, William Hahn, Todd Golub, Jesse Boehm. Accelerating prediction of pediatric and rare cancer vulnerabilities using next-generation cancer models [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 1953. doi:10.1158/1538-7445.AM2017-1953

Abstract 4367: Accelerating prediction of tumor vulnerabilities using next-generation cancer models

The mapping of cancer genomes is rapidly approaching completion. The genomic information encoded by individual patients’ tumors should, in principle, provide a guide for predicting dependencies, but our ability to do so is suboptimal. The challenge stems from the absence of clinical data relating genotypes with dependencies since most cancer mutations are rare and our arsenal of cancer drugs is incomplete. If it was possible to build a preclinical ‘cancer dependency map’ at a scale that captured the genomic diversity of cancer (for instance, models of all genotypes tested for genetic and small-molecule dependencies), it should be feasible to improve dependency predictions. New technologies (e.g. CRISPR/Cas9 libraries) make such an effort now feasible. However, we lack a sufficient diversity of cancer models derived directly from patient samples to reflect the genetic diversity of cancer and the ability to systematically create functional data for each cancer patient to expand the map.

In an attempt to overcome these obstacles, we have established an industry-scale pipeline to generate new cancer models directly from patient samples, a “Cancer Cell Line Factory”. We have processed over 620 samples from 400 patients across 16 cancer types through this pipeline with a 25% success rate overall. To optimize conditions for each tumor type, we have systematically compared published cell line generation methods with standard approaches and captured all information with a data management system that will enhance the ability to predict optimal ex vivo propagation conditions for future samples. In all, we report the successful derivation of over 100 new genomically confirmed cancer and normal cell lines, including a series of unique pediatric cancer models derived from rare tumors.

We hypothesized that novel patient-derived cultures could be used to enhance dependency predictions. To test this hypothesis, we tested dependencies of 65 of these novel cultures against an identical set of 440 small molecules that were previously tested against 860 existing cancer cell lines. Our results suggest that dependency data generated with novel cell cultures is potentially backwards-compatible with existing small molecule dependency datasets. Finally, we demonstrate proof-of-concept that such new models can successfully used in CRISPR-Cas9 screens and integrate results with small molecule sensitivities to uncover CDK4 and XPO1 dependencies in a rare pediatric undifferentiated sarcoma. In aggregate, these proof-of-concept studies demarcate a path by which pre-clinical dependency maps may enhance clinical dependency predictions from genomic data alone.

Citation Format: Yuen-Yi Tseng, Andrew Hong, Paula Keskula, Shubhroz Gill, Jaime Cheah, Grigoriy Kryukov, Aviad Tsherniak, Francisca Vazquez, Glenn Cowley, Coyin Oh, Anson Peng, Abeer Sayeed, Rebecca Deasy, Peter Ronning, Philip Kantoff, Levi Garraway, Mark Rubin, Calvin Kuo, Sidharth Puram, Adi Gazdar, Filemon Dela Cruz, Adam Bass, Nikhil Wagle, Keith Ligon, Katherine Janeway, David Root, Stuart Schreiber, Paul Clemons, Aly Shamji, William Hahn, Todd Golub, Jesse S. Boehm. Accelerating prediction of tumor vulnerabilities using next-generation cancer models. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4367.

Abstract OT2-05-03: The metastatic breast cancer project: A national direct-to-patient research initiative to accelerate genomics research

Over the past decade, genomic characterization of tumors has shed enormous light on the molecular underpinnings of cancer. These discoveries have led to the development of novel therapies and preventive measures that have already revolutionized cancer care. Despite this progress, the genomics of metastatic breast cancer (MBC), one of the leading causes of cancer death in the U.S., remains poorly understood.

The challenge in studying tumor samples from patients with MBC has been that the tumors from most patients are not available for research, largely because the vast majority of patients are cared for in community settings where genomics studies are not typically conducted. To address this, we have launched a nationwide study, The Metastatic Breast Cancer Project, which seeks to empower patients to accelerate cancer research through sharing their samples and clinical information. We have developed an outreach program in collaboration with MBC advocacy organizations to connect MBC patients around the country with genomics research performed at the Broad Institute, allowing them to participate regardless of where they live.

Working with MBC patients and advocates, we designed a website (www.mbcproject.org) with an online questionnaire that allows patients with MBC to provide information about themselves and their cancer. Based on their answers, patients are offered an electronic consent form that explains the risks and benefits of the study and asks for permission to obtain a portion of their stored tumor tissue, a saliva sample, and copies of their medical records. For patients who consent, our clinical research team contacts their physicians and obtains copies of their medical records, which are reviewed to confirm eligibility. Enrolled patients are sent a saliva kit and asked to mail back a saliva sample, which is used to extract germline DNA. The clinical research team also contacts the patient's pathology department and requests a portion of the tumor to be sent to the Broad Institute for genomic analysis. Whole exome and transcriptome sequencing is performed on tumor and germline DNA. Sequencing data are linked to de-identified clinical information, and the resulting data are used to identify drivers of tumorigenesis, mechanisms of response and resistance to therapies, and diagnostic, prognostic, and therapeutic biomarkers. The database of clinically annotated genomic information will be shared with the NIH and the cancer research community. Study updates and discoveries are shared at regular intervals with all patients who complete the initial questionnaire.

This direct-to-patient approach should be particularly enabling for the identification of patients with rare phenotypes or clinical behavior. For this reason, the first cohorts being studied are patients with extraordinary responses to therapies and patients who present with de novo MBC. Additional cohorts will be added in the future, including young women with MBC and patients with drug-resistant MBC. This project seeks to establish a patient-researcher partnership to accelerate genomic discoveries and improve outcomes in MBC, and may ultimately serve as a means to build a new clinical and translational research model for all patients with cancer.

Citation Format: Wagle N, Painter CA, Ilzarbe M, Van Allen EM, Frank E, Oh C, Krevalin M, Lloyd M, Anderka K, Kryukov G, Boehm JS, Winer E, Lander ES, Golub TR. The metastatic breast cancer project: A national direct-to-patient research initiative to accelerate genomics research. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr OT2-05-03.

Oncogenic and drug-sensitive NTRK1 rearrangements in lung cancer

We identified novel gene fusions in patients with lung cancer harboring the kinase domain of the NTRK1 gene that encodes the TRKA receptor. Both the MPRIP-NTRK1 and CD74-NTRK1 fusions lead to constitutive TRKA kinase activity and are oncogenic. Treatment of cells expressing NTRK1 fusions with inhibitors of TRKA kinase activity inhibited autophosphorylation of TRKA and cell growth. Three of 91 lung cancer patients (3.3%), without known oncogenic alterations, assayed by NGS or FISH demonstrated evidence of NTRK1 gene fusions.

Abstract 1682: Uncovering signals of somatic selection through whole exome and whole genome sequencing of lung adenocarcinoma

We have sequenced the exomes of over 100 and the genomes of over 20 lung adenocarcinoma tumor-normal specimen pairs. We performed hybrid capture exome sequencing of nearly 18,000 genes to >100X median per-sample coverage with 76bp paired-end reads. We performed whole genome sequencing achieving 60X median tumor and 20X median normal coverage with 350bp median insert size and 101bp paired-end reads. Our exome analysis yielded over 50,000 substitution and small indel coding events, with a mean somatic mutation rate of 10-11 events / MB. This resulted in over 300 non-synonymous coding events per patient, most of which were presumed to be passenger mutations unrelated to tumorigenesis. This high mutational load required us to develop novel statistical approaches (MutSig, Lawrence et al, in preparation) to identify putative lung adenocarcinoma driver genes under positive somatic selection. We constructed a complex neutral mutation model that considered sequence context and several additional genomic covariates shown to mediate gene to gene inhomogeneities of passenger mutation rates. Identification of significant deviations from this background model allowed us to recover almost all known frequently mutated lung adenocarcinoma genes, including TP53, KRAS, STK11, PIK3CA, EGFR, ERBB2, RB1, SMARCA4, and KEAP1, as well as a host of novel putative driver genes. We applied similar principles to identify pathways and sub-networks of genes undergoing apparent positive selection in lung adenocarcinoma. Whole genome analysis yielded several high-confidence in-frame protein fusion and promoter-gene fusion events enriched in tumor vs normal specimens. We also found large numbers of somatic substitution and indel events in promoters, enhancers, and non-coding DNA elements and identified putative sites of somatic retrotransposition in our whole genome data. Overall, our study eclipses previous large-scale characterization of somatic sequence variation (Refs. 1-3) in lung adenocarcinoma by at least an order of magnitude. Using novel methods adapted to the analysis of high-mutation rate tumor types (lung squamous cell carcinoma, melanoma, colorectal cancer), we are able to recover signals of selection in both known and novel genes and pathways. Our results illuminate novel lung adenocarcinoma tumor biology and provide targets for therapeutic and diagnostic investigation. References 1. Ding et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature (2008) vol. 455 (7216) pp. 1069-75 2. Kan et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature (2010) vol. 466 (7308) pp. 869-73 3. Lee et al. The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature (2010) vol. 465 (7297) pp. 473-7

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1682. doi:1538-7445.AM2012-1682

Separating the wheat from the chaff: unbiased filtering of background tandem mass spectra improves protein identification

Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.