Abstract SY35-02: Phenotypic heterogeneity and instability of human serous ovarian cancer tumor-initiating cells
Bibliographic record
Abstract
Abstract Serous ovarian cancer (SOC) is the leading cause of morbidity/mortality from gynecologic malignancy. Current therapies increase survival significantly, yet the vast majority of SOC patients (70-90%) die of their disease. Patients almost always respond to initial courses of standard-of-care (platinum- and taxane-based) chemotherapy. Unfortunately, however, drug resistance almost always develops, resulting in the death of the patient. Two competing theories have been proposed to account for tumor initiation in SOC (and other malignancies); these theories also have important implications for therapy resistance. In the stochastic model, most, if not all, tumor cells can self-renew indefinitely; consequently, nearly all tumor cells provide a target population for the acquisition of drug resistance mutations. By contrast, the cancer stem cell (CSC) hypothesis holds that only a subset of tumor cells can initiate and maintain the tumor. Moreover, CSC might be intrinsically more resistant to some/many drugs that are effective against other (“bulk”) tumor cells. These two models differ fundamentally in their view of tumor-initiating cells (TIC). The essential feature of the CSC model is that tumors are organized hierarchically, such that TIC can be prospectively distinguished from non-TIC by phenotype. If the CSC model holds, it should be possible to identify and purify a stable cell population with the unique ability to generate serially transplantable tumors that recreate the heterogeneity of the initial malignancy. By contrast, the stochastic model predicts that TIC distribute into all cell fractions. Many studies of ovarian carcinogenesis and drug response have used immortalized cell lines grown for long periods of time in serum-containing culture. However, the extent to which these cells represent the biology of SOC is unclear. Many SOC lines do not reproduce serous histology when propagated as xenografts in immune-compromised mice; others cannot even give rise to xenografts. Even in culture, few of these lines show evidence of the cytologic and immunologic heterogeneity typically seen in primary tumors. Consequently, these lines might not be adequate for testing new therapies for SOC. Moreover, it is not clear that immortalized cell lines represent valid models for evaluating the CSC model or for studying TIC in SOC. Notably, studies of several other malignancies have shown that TIC as defined using immortalized cell lines do not have the same phenotype as those defined in xenografts using primary patient samples. Improving outcome for SOC patients will require better understanding of SOC pathogenesis and drug resistance, using assay systems that reflect the genetic and cellular diversity of human SOC more faithfully than conventional ovarian cancer cell lines. To this end, we have established a large collection of primary human SOC samples and developed a robust, quantitative assay for SOC tumor-initiating cells (TIC). Using this assay, we find that TICs are rare when assayed in either NOD/SCID or NOD/SCID/IL2Rγ−/− (NSG) mice. TIC frequency varies substantially between patients, although it is similar in primary ovarian masses and omental metastases, suggesting that TIC frequency is an intrinsic property of given ovarian tumor classes. For instance, CD133 marks all TICs from several primary SOC cases. However, in other cases, substantial TIC activity is found in both the CD133+ and CD133− fractions, whereas still other cases have exclusively CD133− TICs. Furthermore, the TIC phenotype can change in xenografts: primary tumors in which all TICs are CD133+ can give rise to xenografts that contain substantial numbers of CD133− TICs. Our results highlight the need for quantitative rigor in the evaluation of TICs and for caution when using passaged xenografts for such studies. Furthermore, although our data suggest that SOC conforms to the CSC hypothesis, the heterogeneity of the TIC phenotype may complicate its clinical application. To address whether instability in the TIC phenotype may be due to the acquisition of genetic alterations in the CD133- compared to the CD133+ fraction, we analyzed copy number alterations in CD133 positive and negative-derived xenografts. Preliminary analysis suggests that most genetic alterations are common in both fractions; however, in 3/6 cases, additional genetic alterations were observed in the CD133- fraction, suggesting the emergence of CD133- TIC might be, at least in part, genetically driven. The applicability of this observation to additional cases and primary sorted cells (i.e. those that were used to establish the xenografts), as well as the mechanism of genetic instability is currently under investigation. We used expression microarrays to examine differences between CD133+ and CD133- populations. We find that >1000 probes are differentially expressed in these two cell subsets, confirming that they are distinct cell populations. We have identified several putatively targetable pathways and transcription factor networks with altered expression in TIC-enriched fractions. The biological roles of these pathways in HG-SOC, specifically in the TIC compartment, are under investigation. Finally, we performed high-throughput flow cytometry: involving independent analysis of 365 cell surface markers to identify proteins expressed on all or subsets of HG-SOC cells. We have combined these analyses with mass cytometry (collaboration with Nolan's lab, Stanford University). Mass cytometry allows deep profiling of cell attributes and function using a novel multi-parametric approach combining flow cytometry with mass spectrometry that allows examination of up to 35 cell surface and/or intracellular markers on a single cell. Examination of 40 primary ovarian cancer samples has provided further supportive evidence for inter- and intra-patient heterogeneity in HG-SOC. Current analyses focus on biological validation of identified subpopulations marked by a series of cell surface and putative “stem cell” gene sets. 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 SY35-02. doi:1538-7445.AM2012-SY35-02
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".