Intent-to-treat leukemia remission by CD19 CAR T cells of defined formulation and dose in children and young adults.

Transitioning CD19-directed chimeric antigen receptor (CAR) T cells from early-phase trials in relapsed patients to a viable therapeutic approach with predictable efficacy and low toxicity for broad application among patients with high unmet need is currently complicated by product heterogeneity resulting from transduction of undefined T-cell mixtures, variability of transgene expression, and terminal differentiation of cells at the end of culture. A phase 1 trial of 45 children and young adults with relapsed or refractory B-lineage acute lymphoblastic leukemia was conducted using a CD19 CAR product of defined CD4/CD8 composition, uniform CAR expression, and limited effector differentiation. Products meeting all defined specifications occurred in 93% of enrolled patients. The maximum tolerated dose was 106 CAR T cells per kg, and there were no deaths or instances of cerebral edema attributable to product toxicity. The overall intent-to-treat minimal residual disease-negative (MRD-) remission rate for this phase 1 study was 89%. The MRD- remission rate was 93% in patients who received a CAR T-cell product and 100% in the subset of patients who received fludarabine and cyclophosphamide lymphodepletion. Twenty-three percent of patients developed reversible severe cytokine release syndrome and/or reversible severe neurotoxicity. These data demonstrate that manufacturing a defined-composition CD19 CAR T cell identifies an optimal cell dose with highly potent antitumor activity and a tolerable adverse effect profile in a cohort of patients with an otherwise poor prognosis. This trial was registered at www.clinicaltrials.gov as #NCT02028455.

Evaluation of TCR Gene Editing Achieved by TALENs, CRISPR/Cas9, and megaTAL Nucleases.

Present adoptive immunotherapy strategies are based on the re-targeting of autologous T-cells to recognize tumor antigens. As T-cell properties may vary significantly between patients, this approach can result in significant variability in cell potency that may affect therapeutic outcome. More consistent results could be achieved by generating allogeneic cells from healthy donors. An impediment to such an approach is the endogenous T-cell receptors present on T-cells, which have the potential to direct dangerous off-tumor antihost reactivity. To address these limitations, we assessed the ability of three different TCR-α-targeted nucleases to disrupt T-cell receptor expression in primary human T-cells. We optimized the conditions for the delivery of each reagent and assessed off-target cleavage. The megaTAL and CRISPR/Cas9 reagents exhibited the highest disruption efficiency combined with low levels of toxicity and off-target cleavage, and we used them for a translatable manufacturing process to produce safe cellular substrates for next-generation immunotherapies.

Functional Tuning of CARs Reveals Signaling Threshold above Which CD8+ CTL Antitumor Potency Is Attenuated due to Cell Fas-FasL-Dependent AICD.

Chimeric antigen receptor (CAR) development is biased toward selecting constructs that elicit the highest magnitude of T-cell functional outputs. Here, we show that components of CAR extracellular spacer and cytoplasmic signaling domain modulate, in a cooperative manner, the magnitude of CD8(+)CTL activation for tumor-cell cytolysis and cytokine secretion. Unexpectedly, CAR constructs that generate the highest in vitro activity, either by extracellular spacer length tuning or by the addition of cytoplasmic signaling modules, exhibit attenuated antitumor potency in vivo, whereas CARs tuned for moderate signaling outputs mediate tumor eradication. Recursive CAR triggering renders CTLs expressing hyperactive CARs highly susceptible to activation-induced cell death (AICD) as a result of augmented FasL expression. CAR tuning using combinations of extracellular spacers and cytoplasmic signaling modules, which limit AICD of CD8(+)CTLs, may be a critical parameter for achieving clinical activity against solid tumors.

A targeted proteomics-based pipeline for verification of biomarkers in plasma.

High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.

Tumor microenvironment-derived proteins dominate the plasma proteome response during breast cancer induction and progression.

Tumor development relies upon essential contributions from the tumor microenvironment and host immune alterations. These contributions may inform the plasma proteome in a manner that could be exploited for cancer diagnosis and prognosis. In this study, we employed a systems biology approach to characterize the plasma proteome response in the inducible HER2/neu mouse model of breast cancer during tumor induction, progression, and regression. Mass spectrometry data derived from approximately 1.6 million spectra identified protein networks involved in wound healing, microenvironment, and metabolism that coordinately changed during tumor development. The observed alterations developed prior to cancer detection, increased progressively with tumor growth and reverted toward baseline with tumor regression. Gene expression and immunohistochemical analyses suggested that the cancer-associated plasma proteome was derived from transcriptional responses in the noncancerous host tissues as well as the developing tumor. The proteomic signature was distinct from a nonspecific response to inflammation. Overall, the developing tumor simultaneously engaged a number of innate physiologic processes, including wound repair, immune response, coagulation and complement cascades, tissue remodeling, and metabolic homeostasis that were all detectable in plasma. Our findings offer an integrated view of tumor development relevant to plasma-based strategies to detect and diagnose cancer.

Plasma proteome profiles associated with inflammation, angiogenesis, and cancer.

Tumor development is accompanied by a complex host systemic response, which includes inflammatory and angiogenic reactions. Both tumor-derived and systemic response proteins are detected in plasma from cancer patients. However, given their non-specific nature, systemic response proteins can confound the detection or diagnosis of neoplasia. Here, we have applied an in-depth quantitative proteomic approach to analyze plasma protein changes in mouse models of subacute irritant-driven inflammation, autoreactive inflammation, and matrix associated angiogenesis and compared results to previously described findings from mouse models of polyoma middle T-driven breast cancer and Pdx1-Cre Kras(G12D) Ink4a/Arf (lox/lox)-induced pancreatic cancer. Among the confounding models, approximately 1/3 of all quantified plasma proteins exhibited a significant change in abundance compared to control mice. Of the proteins that changed in abundance, the majority were unique to each model. Altered proteins included those involved in acute phase response, inflammation, extracellular matrix remodeling, angiogenesis, and TGFß signaling. Comparison of changes in plasma proteins between the confounder models and the two cancer models revealed proteins that were restricted to the cancer-bearing mice, reflecting the known biology of these tumors. This approach provides a basis for distinguishing between protein changes in plasma that are cancer-related and those that are part of a non-specific host response.

Proteome and transcriptome profiles of a Her2/Neu-driven mouse model of breast cancer.

PURPOSE: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. EXPERIMENTAL DESIGN: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. RESULTS: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. CONCLUSIONS AND CLINICAL RELEVANCE: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for multiple reaction monitoring-MS. The availability of these datasets will contribute positively to clinical proteomics.

Mapping tissue-specific expression of extracellular proteins using systematic glycoproteomic analysis of different mouse tissues.

Due to their easy accessibility, proteins outside of the plasma membrane represent an ideal but untapped resource for potential drug targets or disease biomarkers. They constitute the major biochemical class of current therapeutic targets and clinical biomarkers. Recent advances in proteomic technologies have fueled interest in analysis of extracellular proteins such as membrane proteins, cell surface proteins, and secreted proteins. However, unlike the gene expression analyses from a variety of tissues and cells using genomic technologies, quantitative proteomic analysis of proteins from various biological sources is challenging due to the high complexity of different proteomes and the lack of robust and consistent methods for analyses of different tissue sources, especially for specific enrichment of extracellular proteins. Since most extracellular proteins are modified by oligosaccharides, the population of glycoproteins therefore represents the majority of extracellular proteomes. Here, we quantitatively analyzed glycoproteins and determined the expression patterns of extracellular proteins from 12 mouse tissues using solid-phase extraction of N-linked glycopeptides and liquid chromatography-tandem mass spectrometry. We identified peptides enclosing 1231 possible N-linked glycosites from 826 unique proteins. We further determined the expression pattern of formerly N-linked glycopeptides and identified extracellular glycoproteins specifically expressed in each tissue. Furthermore, the tissue specificities of the overexpressed glycoproteins in a mouse skin tumor model were determined by comparing them to the quantitative protein expression from the different tissues. These skin tumor-specific extracellular proteins might serve as potential candidates for cell surface drug targets or disease-specific protein markers.

A mouse model repository for cancer biomarker discovery.

Early detection of cancer using biomarkers obtained from blood or other easily accessible tissues would have a significant impact on reducing cancer mortality. However, identifying new blood-based biomarkers has been hindered by the dynamic complexity of the human plasma proteome, confounded by genetic and environmental variability, and the scarcity of high quality controlled samples. In this report, we discuss a new paradigm for biomarker discovery through the use of mouse models. Inbred mouse models of cancer recapitulate many critical features of human cancer, while eliminating sources of environmental and genetic variability. The ability to collect samples from highly matched cases and controls under identical conditions further reduces variability which is critical for successful biomarker discovery. We describe the establishment of a repository containing tumor, plasma, urine, and other tissues from 10 different mouse models of human cancer, including two breast, two lung, two prostate, two gastrointestinal, one ovarian, and one skin tumor model. We present the overall design of this resource and its potential use by the research community for biomarker discovery.

Tumor suppression by p53 in the absence of Atm.

Oncogenes can induce p53 through a signaling pathway involving p19/Arf. It was recently proposed that oncogenes can also induce DNA damage, and this can induce p53 through the Atm DNA damage pathway. To assess the relative roles of Atm, Arf, and p53 in the suppression of Ras-driven tumors, we examined susceptibility to skin carcinogenesis in 7,12-dimethylbenz(a)anthracene/12-O-tetradecanoylphorbol-13-acetate (TPA)-treated Atm- and p53-deficient mice and compared these results to previous studies on Arf-deficient mice. Mice with epidermal-specific deletion of p53 showed increased papilloma number and progression to malignant invasive carcinomas compared with wild-type littermates. In contrast, Atm-deficient mice showed no increase in papilloma number, growth, or malignant progression. gamma-H2AX and p53 levels were increased in both Atm(+/+) and Atm(-/-) papillomas, whereas Arf(-/-) papillomas showed much lower p53 expression. Thus, although there is evidence of DNA damage, signaling through Arf seems to regulate p53 in these Ras-driven tumors. In spontaneous and radiation-induced lymphoma models, tumor latency was accelerated in Atm(-/-)p53(-/-) compound mutant mice compared with the single mutant Atm(-/-) or p53(-/-) mice, indicating cooperation between loss of Atm and loss of p53. Although p53-mediated apoptosis was impaired in irradiated Atm(-/-) lymphocytes, p53 loss was still selected for during lymphomagenesis in Atm(-/-) mice. In conclusion, in these models of oncogene- or DNA damage-induced tumors, p53 retains tumor suppressor activity in the absence of Atm.

Plasma proteome profiling of a mouse model of breast cancer identifies a set of up-regulated proteins in common with human breast cancer cells.

We have applied an in-depth quantitative proteomic approach, combining isotopic labeling extensive intact protein separation and mass spectrometry, for high confidence identification of protein changes in plasmas from a mouse model of breast cancer. We hypothesized that a wide spectrum of proteins may be up-regulated in plasma with tumor development and that comparisons with proteins expressed in human breast cancer cell lines may identify a subset of up-regulated proteins in common with proteins expressed in breast cancer cell lines that may represent candidate biomarkers for breast cancer. Plasma from PyMT transgenic tumor-bearing mice and matched controls were obtained at two time points during tumor growth. A total of 133 proteins were found to be increased by 1.5-fold or greater at one or both time points. A comparison of this set of proteins with published findings from proteomic analysis of human breast cancer cell lines yielded 49 proteins with increased levels in mouse plasma that were identified in breast cancer cell lines. Pathway analysis comparing the subset of up-regulated proteins known to be expressed in breast cancer cell lines with other up-regulated proteins indicated a cancer related function for the former and a host-response function for the latter. We conclude that integration of proteomic findings from mouse models of breast cancer and from human breast cancer cell lines may help identify a subset of proteins released by breast cancer cells into the circulation and that occur at increased levels in breast cancer.

Identification of glycoproteins from mouse skin tumors and plasma.

Plasma has been the focus of testing different proteomic technologies for the identification of biomarkers due to its ready accessibility. However, it is not clear if direct proteomic analysis of plasma can be used to discover new marker proteins from tumor that are associated with tumor progression. Here, we reported that such proteins can be detected in plasma in a chemical induced skin cancer mouse model. We analyzed glycoproteins from both benign papillomas and malignant carcinomas from mice using our recently developed platform, solid-phase extraction of glycopeptides (SPEG) and mass spectrometry, and identified 463 unique N-linked glycosites from 318 unique glycoproteins. These include most known extracellular proteins that have been reported to play roles in skin cancer development such as thrombospondin, cathepsins, epidermal growth factor receptor, cell adhesion molecules, cadherins, integrins, tuberin, fibulin, TGFß receptor, etc. We further investigated whether these tumor proteins could be detected in plasma from tumor bearing mice using isotope labeling and 2D-LC-MALDI-MS/MS. Two tumor glycoproteins, Tenascin-C and Arylsulfatase B, were identified and quantified successfully in plasma from tumor bearing mice. This result indicates that analysis of tumor associated proteins in tumors and plasma by method using glycopeptide capture, isotopic labeling, and mass spectrometry can be used as a discovery tool to identify candidate tumor proteins that may be detected in plasma.

Differential Plasma Glycoproteome of p19 Skin Cancer Mouse Model Using the Corra Label-Free LC-MS Proteomics Platform.

A proof-of-concept demonstration of the use of label-free quantitative glycoproteomics for biomarker discovery workflow is presented here, using a mouse model for skin cancer as an example. Blood plasma was collected from 10 control mice, and 10 mice having a mutation in the p19(ARF) gene, conferring them high propensity to develop skin cancer after carcinogen exposure. We enriched for N-glycosylated plasma proteins, ultimately generating deglycosylated forms of the modified tryptic peptides for liquid chromatography mass spectrometry (LC-MS) analyses. LC-MS runs for each sample were then performed with a view to identifying proteins that were differentially abundant between the two mouse populations. We then used a recently developed computational framework, Corra, to perform peak picking and alignment, and to compute the statistical significance of any observed changes in individual peptide abundances. Once determined, the most discriminating peptide features were then fragmented and identified by tandem mass spectrometry with the use of inclusion lists. We next assessed the identified proteins to see if there were sets of proteins indicative of specific biological processes that correlate with the presence of disease, and specifically cancer, according to their functional annotations. As expected for such sick animals, many of the proteins identified were related to host immune response. However, a significant number of proteins also directly associated with processes linked to cancer development, including proteins related to the cell cycle, localisation, trasport, and cell death. Additional analysis of the same samples in profiling mode, and in triplicate, confirmed that replicate MS analysis of the same plasma sample generated less variation than that observed between plasma samples from different individuals, demonstrating that the reproducibility of the LC-MS platform was sufficient for this application. These results thus show that an LC-MS-based workflow can be a useful tool for the generation of candidate proteins of interest as part of a disease biomarker discovery effort.

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer.

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.

A pathway in quiescent cells that controls p27Kip1 stability, subcellular localization, and tumor suppression.

We have created two knock-in mouse models to study the mechanisms that regulate p27 in normal cells and cause misregulation of p27 in tumors: p27(S10A), in which Ser10 is mutated to Ala; and p27(CK-), in which point mutations abrogate the ability of p27 to bind cyclins and CDKs. These two mutant alleles identify steps in a pathway that controls the proteasomal degradation of p27 uniquely in quiescent cells: Dephosphorylation of p27 on Ser10 inhibits p27 nuclear export and promotes its assembly into cyclin-CDK complexes, which is, in turn, necessary for p27 turnover. We further show that Ras-dependent lung tumorigenesis is associated with increased phosphorylation on Ser10 and cytoplasmic mislocalization of p27. Indeed, we find that p27(S10A) is refractory to Ras-induced cytoplasmic translocation and that p27(S10A) mice are tumor resistant. Thus, phosphorylation of p27 on Ser10 is an important event in the regulation of the tumor suppressor function of p27.

High throughput quantitative analysis of serum proteins using glycopeptide capture and liquid chromatography mass spectrometry.

It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible, and robust to detect potential biomarkers below the level of highly expressed proteins, generate data sets that are comparable between experiments and laboratories, and have high throughput to support statistical studies. Here we report a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of peptides that are N-linked glycosylated in the intact protein, the analysis of these now deglycosylated peptides by liquid chromatography electrospray ionization mass spectrometry, and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced and the sensitivity and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We provide data that document the performance of the method and show that sera from untreated normal mice and genetically identical mice with carcinogen-induced skin cancer can be unambiguously discriminated using unsupervised clustering of the resulting peptide patterns. We further identify, by tandem mass spectrometry, some of the peptides that were consistently elevated in cancer mice compared with their control littermates.

p19Arf suppresses growth, progression, and metastasis of Hras-driven carcinomas through p53-dependent and -independent pathways.

Ectopic expression of oncogenes such as Ras induces expression of p19(Arf), which, in turn, activates p53 and growth arrest. Here, we used a multistage model of squamous cell carcinoma development to investigate the functional interactions between Ras, p19(Arf), and p53 during tumor progression in the mouse. Skin tumors were induced in wild-type, p19(Arf)-deficient, and p53-deficient mice using the DMBA/TPA two-step protocol. Activating mutations in Hras were detected in all papillomas and carcinomas examined, regardless of genotype. Relative to wild-type mice, the growth rate of papillomas was greater in p19(Arf)-deficient mice, and reduced in p53-deficient mice. Malignant conversion of papillomas to squamous cell carcinomas, as well as metastasis to lymph nodes and lungs, was markedly accelerated in both p19 (Arf)- and p53-deficient mice. Thus, p19(Arf) inhibits the growth rate of tumors in a p53-independent manner. Through its regulation of p53, p19(Arf) also suppresses malignant conversion and metastasis. p53 expression was upregulated in papillomas from wild-type but not p19( Arf)-null mice, and p53 mutations were more frequently seen in wild-type than in p19( Arf)-null carcinomas. This indicates that selection for p53 mutations is a direct result of signaling from the initiating oncogenic lesion, Hras, acting through p19(Arf).

BDNF activated TrkB/IRR receptor chimera promotes survival of sympathetic neurons through Ras and PI-3 kinase signaling.

Insulin receptor-related receptor (IRR) expression is tightly coupled to the nerve growth factor (NGF) receptor, TrkA, throughout development. Expression of both receptors is primarily localized to neural crest derived sensory and sympathetic neurons. In contrast to TrkA, however, the physiological ligand for IRR is unknown. To analyze the intracellular signaling and potential function of the orphan IRR in neurons, an adenovirus expressing a TrkB/IRR chimeric receptor was used to infect cultured mouse superior cervical ganglion neurons that normally require NGF for survival. Brain derived neurotrophic factor (BDNF)-activated TrkB/IRR induced neuronal survival. We utilized numerous receptor mutants in order to identify the intracellular domains of IRR necessary for signaling and neuron survival. Finally, we employed adenovirus encoding dominant negative forms of the extracellular signal-regulated kinase (ERK) signaling cascade to demonstrate that IRR, like TrkA, requires ras activation to promote neuron survival. Therefore, by use of the chimeric TrkB/IRR receptor, we have demonstrated the ability of IRR to elicit activation of signaling cascades resulting in a biological response in superior cervical ganglion (SCG) neurons.