PhD Candidate in the Cancer Biology program. Joint member of the Angelo and Curtis labs working to integrate imaging and sequencing data to better understand the tumor microenvironment in breast cancer.
Current Research and Scholarly Interests
Using deep learning to analyze multiplexed imaging data; profiling the tumor microenvironment to predict response and resistance to checkpoint blockade; integrating genomics, transcriptomics, and imaging to understand how changes in DNA and RNA affect phenotypes at the protein level
MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure.
2019; 5 (10): eaax5851
Understanding tissue structure and function requires tools that quantify the expression of multiple proteins while preserving spatial information. Here, we describe MIBI-TOF (multiplexed ion beam imaging by time of flight), an instrument that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. We demonstrate quantitative, full periodic table coverage across a five-log dynamic range, imaging 36 labeled antibodies simultaneously with histochemical stains and endogenous elements. We image fields of view up to 800 mum * 800 mum at resolutions down to 260 nm with sensitivities approaching single-molecule detection. We leverage these properties to interrogate intrapatient heterogeneity in tumor organization in triple-negative breast cancer, revealing regional variability in tumor cell phenotypes in contrast to a structured immune response. Given its versatility and sample back-compatibility, MIBI-TOF is positioned to leverage existing annotated, archival tissue cohorts to explore emerging questions in cancer, immunology, and neurobiology.
View details for DOI 10.1126/sciadv.aax5851
View details for PubMedID 31633026
Patient-derived xenografts undergo mouse-specific tumor evolution
2017; 49 (11): 1567-+
Patient-derived xenografts (PDXs) have become a prominent cancer model system, as they are presumed to faithfully represent the genomic features of primary tumors. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We observed rapid accumulation of CNAs during PDX passaging, often due to selection of preexisting minor clones. CNA acquisition in PDXs was correlated with the tissue-specific levels of aneuploidy and genetic heterogeneity observed in primary tumors. However, the particular CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients. Several CNAs recurrently observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Notably, the genomic stability of PDXs was associated with their response to chemotherapy and targeted drugs. These findings have major implications for PDX-based modeling of human cancer.
View details for DOI 10.1038/ng.3967
View details for Web of Science ID 000413909800005
View details for PubMedID 28991255
View details for PubMedCentralID PMC5659952
- Genomic landscape of high-grade meningiomas NPJ GENOMIC MEDICINE 2017; 2
DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.
2021; 18 (1): 43–45
Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console ; a persistent deployment is available at https://deepcell.org/ .
View details for DOI 10.1038/s41592-020-01023-0
View details for PubMedID 33398191
Single-cell metabolic profiling of human cytotoxic T cells.
Cellular metabolism regulates immune cell activation, differentiation and effector functions, but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. In this study, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, termed single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using high-dimensional antibody-based technologies. We employed mass cytometry (cytometry by time of flight, CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naive and memory CD8+ T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with multiplexed ion beam imaging by time of flight (MIBI-TOF), we uncovered the spatial organization of metabolic programs in human tissues, which indicated exclusion of metabolically repressed immune cells from the tumor-immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.
View details for DOI 10.1038/s41587-020-0651-8
View details for PubMedID 32868913
The Society for Immunotherapy in Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation.
Journal for immunotherapy of cancer
2020; 8 (1)
The interaction between the immune system and tumor cells is an important feature for the prognosis and treatment of cancer. Multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) analyses are emerging technologies that can be used to help quantify immune cell subsets, their functional state, and their spatial arrangement within the tumor microenvironment.The Society for Immunotherapy of Cancer (SITC) convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the optimization and validation of mIHC/mIF assays across platforms.Representative outputs and the advantages and disadvantages of mIHC/mIF approaches, such as multiplexed chromogenic IHC, multiplexed immunohistochemical consecutive staining on single slide, mIF (including multispectral approaches), tissue-based mass spectrometry, and digital spatial profiling are discussed.mIHC/mIF technologies are becoming standard tools for biomarker studies and are likely to enter routine clinical practice in the near future. Careful assay optimization and validation will help ensure outputs are robust and comparable across laboratories as well as potentially across mIHC/mIF platforms. Quantitative image analysis of mIHC/mIF output and data management considerations will be addressed in a complementary manuscript from this task force.
View details for DOI 10.1136/jitc-2019-000155
View details for PubMedID 32414858
- Large expert-curated database for benchmarking document similarity detection in biomedical literature search DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019
Neuronal differentiation and cell-cycle programs mediate response to BET-bromodomain inhibition in MYC-driven medulloblastoma
2019; 10: 2400
BET-bromodomain inhibition (BETi) has shown pre-clinical promise for MYC-amplified medulloblastoma. However, the mechanisms for its action, and ultimately for resistance, have not been fully defined. Here, using a combination of expression profiling, genome-scale CRISPR/Cas9-mediated loss of function and ORF/cDNA driven rescue screens, and cell-based models of spontaneous resistance, we identify bHLH/homeobox transcription factors and cell-cycle regulators as key genes mediating BETi's response and resistance. Cells that acquire drug tolerance exhibit a more neuronally differentiated cell-state and expression of lineage-specific bHLH/homeobox transcription factors. However, they do not terminally differentiate, maintain expression of CCND2, and continue to cycle through S-phase. Moreover, CDK4/CDK6 inhibition delays acquisition of resistance. Therefore, our data provide insights about the mechanisms underlying BETi effects and the appearance of resistance and support the therapeutic use of combined cell-cycle inhibitors with BETi in MYC-amplified medulloblastoma.
View details for DOI 10.1038/s41467-019-10307-9
View details for Web of Science ID 000469909500002
View details for PubMedID 31160565
View details for PubMedCentralID PMC6546744
SvABA: genome-wide detection of structural variants and indels by local assembly
2018; 28 (4): 581–91
Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA's performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs and substantially improves detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (<1000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types and found that short templated-sequence insertions occur in ∼4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized (50-300 bp) SVs.
View details for DOI 10.1101/gr.221028.117
View details for Web of Science ID 000428993500014
View details for PubMedID 29535149
View details for PubMedCentralID PMC5880247
Radiographic prediction of meningioma grade by semantic and radiomic features
2017; 12 (11): e0187908
The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making.A total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44).Twelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62-0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84).We found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.
View details for DOI 10.1371/journal.pone.0187908
View details for Web of Science ID 000415378800037
View details for PubMedID 29145421
View details for PubMedCentralID PMC5690632
Osteoglycin promotes meningioma development through downregulation of NF2 and activation of mTOR signaling
CELL COMMUNICATION AND SIGNALING
2017; 15: 34
Meningiomas are the most common primary intracranial tumors in adults. While a majority of meningiomas are slow growing neoplasms that may cured by surgical resection, a subset demonstrates more aggressive behavior and insidiously recurs despite surgery and radiation, without effective alternative treatment options. Elucidation of critical mitogenic pathways in meningioma oncogenesis may offer new therapeutic strategies. We performed an integrated genomic and molecular analysis to characterize the expression and function of osteoglycin (OGN) in meningiomas and explored possible therapeutic approaches for OGN-expressing meningiomas.OGN mRNA expression in human meningiomas was assessed by RNA microarray and RNAscope. The impact of OGN on cell proliferation, colony formation, and mitogenic signaling cascades was assessed in a human meningioma cell line (IOMM-Lee) with stable overexpression of OGN. Furthermore, the functional consequences of introducing an AKT inhibitor in OGN-overexpressing meningioma cells were assessed.OGN mRNA expression was dramatically increased in meningiomas compared to a spectrum of other brain tumors and normal brain. OGN-overexpressing meningioma cells demonstrated an elevated rate of cell proliferation, cell cycle activation, and colony formation as compared with cells transfected with control vector. In addition, NF2 mRNA and protein expression were both attenuated in OGN-overexpressing cells. Conversely, mTOR pathway and AKT activation increased in OGN-overexpressing cells compared to control cells. Lastly, introduction of an AKT inhibitor reduced OGN expression in meningioma cells and resulted in increased cell death and autophagy, suggestive of a reciprocal relationship between OGN and AKT.We identify OGN as a novel oncogene in meningioma proliferation. AKT inhibition reduces OGN protein levels in meningioma cells, with a concomitant increase in cell death, which provides a promising treatment option for meningiomas with OGN overexpression.
View details for DOI 10.1186/s12964-017-0189-7
View details for Web of Science ID 000411347100002
View details for PubMedID 28923059
View details for PubMedCentralID PMC5604305
- Artificial intelligence in research SCIENCE 2017; 357 (6346): 28
Clinical Identification of Oncogenic Drivers and Copy-Number Alterations in Pituitary Tumors
2017; 158 (7): 2284–91
Pituitary tumors are the second most common adult primary brain tumor, with a variable clinical course. Recent work has identified a number of genetic determinants of pituitary tumor subtypes, which may augment traditional histopathologic classification schemes. We sought to determine whether pituitary tumors could be stratified based on objective molecular characteristics using a clinical genomics assay. We performed a retrospective analysis of patients operated on at the Brigham and Women's Hospital from 2012 to 2016 whose pituitary tumors were profiled using multiplexed next-generation sequencing. We analyzed 127 pituitary tumors, including 114 adenomas, 5 craniopharyngiomas, and 8 tumors of other histologies. We observed recurrent BRAFV600E mutations in papillary craniopharyngiomas, CTNNB1 mutations in adamantinomatous craniopharyngiomas, and activating GNAS mutations in growth hormone-secreting adenomas. Furthermore, we validated the presence of two distinct genomic subclasses in adenomas (i.e., those with disrupted or quiet copy-number profiles) and the significant association of disruption with functional hormone status (P < 0.05). We report the clinical implementation of next-generation sequencing of pituitary tumors. We confirmed previously identified molecular subclasses for these tumors and show that routine screening as part of clinical practice is both feasible and informative. This large-scale proof-of-principle study may help to guide future institutional efforts for pituitary tumor classification as well as the incorporation of such techniques into prospective analysis as part of clinical trials.
View details for DOI 10.1210/en.2016-1967
View details for Web of Science ID 000405105100030
View details for PubMedID 28486603
View details for PubMedCentralID PMC5505210
Clinical targeted exome-based sequencing in combination with genome-wide copy number profiling: precision medicine analysis of 203 pediatric brain tumors
2017; 19 (7): 986–96
Clinical genomics platforms are needed to identify targetable alterations, but implementation of these technologies and best practices in routine clinical pediatric oncology practice are not yet well established.Profile is an institution-wide prospective clinical research initiative that uses targeted sequencing to identify targetable alterations in tumors. OncoPanel, a multiplexed targeted exome-sequencing platform that includes 300 cancer-causing genes, was used to assess single nucleotide variants and rearrangements/indels. Alterations were annotated (Tiers 1-4) based on clinical significance, with Tier 1 alterations having well-established clinical utility. OncoCopy, a clinical genome-wide array comparative genomic hybridization (aCGH) assay, was also performed to evaluate copy number alterations and better define rearrangement breakpoints.Cancer genomes of 203 pediatric brain tumors were profiled across histological subtypes, including 117 samples analyzed by OncoPanel, 146 by OncoCopy, and 60 tumors subjected to both methodologies. OncoPanel revealed clinically relevant alterations in 56% of patients (44 cancer mutations and 20 rearrangements), including BRAF alterations that directed the use of targeted inhibitors. Rearrangements in MYB-QKI, MYBL1, BRAF, and FGFR1 were also detected. Furthermore, while copy number profiles differed across histologies, the combined use of OncoPanel and OncoCopy identified subgroup-specific alterations in 89% (17/19) of medulloblastomas.The combination of OncoPanel and OncoCopy multiplex genomic assays can identify critical diagnostic, prognostic, and treatment-relevant alterations and represents an effective precision medicine approach for clinical evaluation of pediatric brain tumors.
View details for DOI 10.1093/neuonc/now294
View details for Web of Science ID 000403446300018
View details for PubMedID 28104717
View details for PubMedCentralID PMC5570190
Genomic profile of human meningioma cell lines
2017; 12 (5): e0178322
Meningiomas, derived from arachnoid cap cells, are the most common intracranial tumor. High-grade meningiomas, as well as those located at the skull base or near venous sinuses, frequently recur and are challenging to manage. Next-generation sequencing is identifying novel pharmacologic targets in meningiomas to complement surgery and radiation. However, due to the lack of in vitro models, the importance and implications of these genetic variants in meningioma pathogenesis and therapy remain unclear. We performed whole exome sequencing to assess single nucleotide variants and somatic copy number variants in four human meningioma cell lines, including two benign lines (HBL-52 and Ben-Men-1) and two malignant lines (IOMM-Lee and CH157-MN). The two malignant cell lines harbored an elevated rate of mutations and copy number alterations compared to the benign lines, consistent with the genetic profiles of high-grade meningiomas. In addition, these cell lines also harbored known meningioma driver mutations in neurofibromin 2 (NF2) and TNF receptor-associated factor 7 (TRAF7). These findings demonstrate the relevance of meningioma cell lines as a model system, especially as tools to investigate the signaling pathways of, and subsequent resistance to, therapeutics currently in clinical trials.
View details for DOI 10.1371/journal.pone.0178322
View details for Web of Science ID 000402063000035
View details for PubMedID 28552950
View details for PubMedCentralID PMC5446134
Landscape of Genomic Alterations in Pituitary Adenomas
CLINICAL CANCER RESEARCH
2017; 23 (7): 1841–51
Purpose: Pituitary adenomas are the second most common primary brain tumor, yet their genetic profiles are incompletely understood.Experimental Design: We performed whole-exome sequencing of 42 pituitary macroadenomas and matched normal DNA. These adenomas included hormonally active and inactive tumors, ones with typical or atypical histology, and ones that were primary or recurrent.Results: We identified mutations, insertions/deletions, and copy-number alterations. Nearly one-third of samples (29%) had chromosome arm-level copy-number alterations across large fractions of the genome. Despite such widespread genomic disruption, these tumors had few focal events, which is unusual among highly disrupted cancers. The other 71% of tumors formed a distinct molecular class, with somatic copy number alterations involving less than 6% of the genome. Among the highly disrupted group, 75% were functional adenomas or atypical null-cell adenomas, whereas 87% of the less-disrupted group were nonfunctional adenomas. We confirmed this association between functional subtype and disruption in a validation dataset of 87 pituitary adenomas. Analysis of previously published expression data from an additional 50 adenomas showed that arm-level alterations significantly impacted transcript levels, and that the disrupted samples were characterized by expression changes associated with poor outcome in other cancers. Arm-level losses of chromosomes 1, 2, 11, and 18 were significantly recurrent. No significantly recurrent mutations were identified, suggesting no genes are altered by exonic mutations across large fractions of pituitary macroadenomas.Conclusions: These data indicate that sporadic pituitary adenomas have distinct copy-number profiles that associate with hormonal and histologic subtypes and influence gene expression. Clin Cancer Res; 23(7); 1841-51. ©2016 AACR.
View details for DOI 10.1158/1078-0432.CCR-16-0790
View details for Web of Science ID 000398262700023
View details for PubMedID 27707790
View details for PubMedCentralID PMC5380512
Increased expression of programmed death ligand 1 (PD-L1) in human pituitary tumors
2016; 7 (47): 76565–76
Subsets of pituitary tumors exhibit an aggressive clinical courses and recur despite surgery, radiation, and chemotherapy. Because modulation of the immune response through inhibition of T-cell checkpoints has led to durable clinical responses in multiple malignancies, we explored whether pituitary adenomas express immune-related biomarkers that could suggest suitability for immunotherapy. Specifically, programmed death ligand 1 (PD-L1) has emerged as a potential biomarker whose expression may portend more favorable responses to immune checkpoint blockade therapies. We thus investigated the expression of PD-L1 in pituitary adenomas.PD-L1 RNA and protein expression were evaluated in 48 pituitary tumors, including functioning and non-functioning adenomas as well as atypical and recurrent tumors. Tumor infiltrating lymphocyte populations were also assessed by immunohistochemistry.Pituitary tumors express variable levels of PD-L1 transcript and protein. PD-L1 RNA and protein expression were significantly increased in functioning (growth hormone and prolactin-expressing) pituitary adenomas compared to non-functioning (null cell and silent gonadotroph) adenomas. Moreover, primary pituitary adenomas harbored higher levels of PD-L1 mRNA compared to recurrent tumors. Tumor infiltrating lymphocytes were observed in all pituitary tumors and were positively correlated with increased PD-L1 expression, particularly in the functional subtypes.Human pituitary adenomas harbor PD-L1 across subtypes, with significantly higher expression in functioning adenomas compared to non-functioning adenomas. This expression is accompanied by the presence of tumor infiltrating lymphocytes. These findings suggest the existence of an immune response to pituitary tumors and raise the possibility of considering checkpoint blockade immunotherapy in cases refractory to conventional management.
View details for DOI 10.18632/oncotarget.12088
View details for Web of Science ID 000389633400018
View details for PubMedID 27655724
View details for PubMedCentralID PMC5363530