Biology subjects

Levine, D. A.

Publications and source records attributed to Levine, D. A..

6 recordsLinked to original sources

bioRxiv2022

Drug-induced adaptation along a resistance continuum in cancer cells

Advancements in rational drug design over the past decades have consistently produced new cancer therapies, but such treatments are inevitably countered through an adaptive process that fosters therapy resistance. Malignant cells achieve drug resistance through intrinsic and acquired mechanisms, rooted in genetic and non-genetic determinants. In particular, recent work has highlighted the role of intrinsic cellular heterogeneity in the emergence of transient drug-tolerant persister cells that survive drug treatment, as well as non-genetically driven cell plasticity toward stable resistance. However, these models do not account for the role of dose and treatment duration as extrinsic forces in eliciting cancer cell adaptation. Here, we show that these two components together drive the resistance of ovarian cancer cells to targeted therapy along a trajectory of cellular adaptation, that we denote the resistance continuum. We report that gradual dose exposure and prolonged treatment promote a continuous increase in fitness, and show that this process is mediated by evolving transcriptional, epigenetic and genetic changes that promote multiple cell state transitions. The resistance continuum is underpinned by the assembly of gene expression programs and epigenetically reinforced stress response regulation. Using both in vivo and in vitro models, we found that this process involves widespread reprogramming of cell survival pathways, including interferon response, lineage reprogramming, metabolic rewiring and oxidative stress regulation. Together, the resistance continuum reveals the dynamic nature of cellular adaptation, and carries implications for cancer therapies, as initial exposure to lower doses primes cells over time for increased resistance to higher doses. Beyond cancer, such continuous adaptation exposes a basic aspect of cellular plasticity, which may also be deployed in other biological systems such as development, immune response and host-pathogen interactions.

cancer biology↗

bioRxiv2021

Machine-Perception Nanosensor Platform to Detect Cancer Biomarkers

Conventional molecular recognition elements, such as antibodies, present issues for the development of biomolecular assays for use in point-of-care devices, implantable/wearables, and under-resourced settings. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, which are often diagnosed at advanced stages, leading to low survival rates. We investigated the platform for detection in uterine lavage samples, which are enriched with cancer biomarkers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from cancer patients. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.

bioengineering↗

bioRxiv2019

Computed Tomography Measures of Inter-site tumor Heterogeneity for Classifying Outcomes in High-Grade Serous Ovarian Carcinoma: a Retrospective Study

AbstractO_ST_ABSBackgroundC_ST_ABSHigh grade serous ovarian carcinoma shows marked intra-tumoral heterogeneity which is associated with decreased survival and resistance to platinum-based chemotherapy. Pre-treatment quantification of spatial tumor heterogeneity by multiple tissue sampling is not clinically feasible. Using standard-of-care CT imaging to non-invasively quantify heterogeneity could have high clinical utility and would be highly cost-effective. Texture analysis measures local variations in computed tomography (CT) image intensity. Haralick texture methods are typically used to capture the heterogeneity of entire lesions; however, this neglects the possible presence of texture habitats within the lesion, and the differences between metastatic sites. The primary aim of this study was to develop texture analysis of intra-site and inter-site spatial heterogeneity from standard-of-care CT images and to correlate these measures with clinical and genomic features in patients with HGSOC. Methods and findingsWe analyzed the data from a retrospective cohort of 84 patients with HGSOC consisting of 46 patients from Memorial Sloan Kettering Cancer Center (MSKCC) and 38 non-MSKCC cases selected from The Cancer Imaging Archive (TCIA). Inclusion criteria consisted of FIGO stage II-IV HGSOC, attempted primary cytoreductive surgery, intravenous contrast-enhanced CT of abdomen and pelvis performed prior to surgery and availability of molecular tumor data analysed as per the Cancer Genome Atlas (TCGA) Research Network ovarian cancer project. Manual segmentation and image analysis was performed on 463 metastatic tumor sites from 84 patients. In the MSKCC cohort the median number of tumor sites was 7 (interquartile range 5-9) and 4 (interquartile range 3-4) in the TCIA patients. Sub-regions were produced within each tumor site by grouping voxels with similar Haralick texture using the Kernel K-means method. We derived statistical measures of intra- and inter-site tumor heterogeneity (IISTH) including cluster sites entropy (cSE), cluster sites standard deviation (cluDev) and cluster sites dissimilarity (cluDiss) from sub-regions identified within and between individual tumor sites. Unsupervised clustering was used to group patient IISTH measures into low, medium, high, and ultra-high heterogeneity clusters from each cohort. The IISTH measure cluDiss was an independent predictor of progression-free survival (PFS) in multivariable analysis in both datasets (MSKCC hazard ratio [HR] 1.04, 95% CI 1.01-1.06, P = 0.002; TCIA HR 1.05, 95% CI 1.00-1.10, P = 0.049). Low and medium IISTH clusters were associated with longer PFS in multivariable analysis (MSKCC HR 2.94, 90% CI 1.29-6.70, P = 0.009, TCIA HR 5.94, 95% CI 1.05-33.6, P = 0.044). IISTH measures were robust to differences in the CT imaging systems. Average Haralick textures contrast (TCIA HR 1.08, 95% CI 1.01-1.10, P = 0.019) and homogeneity (TCIA HR 1.09, 95% CI 1.02-1.16, P = 0.008) were associated with PFS in mutivariate analysis only in the TCIA dataset. All other average Haralick textures and total tumor volume were not associated with PFS in either dataset. ConclusionsTexture measures of intra- and inter-site tumor heterogeneity from standard of care CT images are correlated with shorter PFS in HGSOC patients. These quantitative methods are independent of the CT imaging system and can thus be applied in clinical practice. The methodology proposed here enables the non-invasive quantification of intra-tumoral heterogeneity and disease stratification for future experimental medicine studies and clinical trials, particularly in cases where total tumour volume and averaged textures have low predictive power. Author summaryO_ST_ABSWhy was this study done?C_ST_ABSO_LITumor heterogeneity is a feature of many solid malignancies including ovarian cancer. C_LIO_LIRecent genomic research suggests that intra-site tumor heterogeneity (heterogeneity within a single tumor site) and inter-site tumor heterogeneity (heterogeneity between different metastatic sites in the same patient) correlate with clinical outcome in HGSOC. C_LI What did the researchers do and find?O_LIWe developed quantitative and non-invasive image-analysis based measures for predicting outcome in HGSOC patients by combining image-based information from within and between multiple tumor sites. C_LIO_LIUsing datasets from two sources, we demonstrate that these image-based tumor heterogeneity measures predict progression free survival in patients with HGSOC. C_LI What do these findings mean?O_LINon-invasive measures of CT image heterogeneity may predict outcomes in HGSOC patients. C_LIO_LIWider application of these CT image heterogeneity measures could prove useful for stratifying patients to different therapies given that total tumour volume and averaged textures have low predictive power. C_LI

bioinformatics↗

bioRxiv2018

Both Fallopian Tube and Ovarian Surface Epithelium Can Act as Cell-of-Origin for High Grade Serous Ovarian Carcinoma

The cell-of-origin of high grade serous ovarian carcinoma (HGSOC) remains controversial, with fallopian tube epithelium (FTE) and ovarian surface epithelium (OSE) each suggested as candidates. Here, by using genetically engineered mouse models and novel organoid systems, we assessed the tumor-forming capacity and properties of FTE and OSE harboring the same oncogenic abnormalities. Combined RB family inactivation (via T121 expression) and Tp53 mutation in Pax8+ FTE caused transformation to Serous Tubal Intraepithelial Carcinoma (STIC), which rapidly metastasized to the ovarian surface. This mouse model was recapitulated by FTE organoids, which, upon orthotopic injection, generated widely metastatic HGSOC. The same genetic lesions in Lgr5+ OSE cells or organoids also caused metastatic HGSOC, although with longer latency and lower penetrance. Comparative transcriptome analysis was consistent with different human HGSOCs arising from FTE and OSE. Furthermore, FTE- and OSE-derived organoids showed differential sensitivity to HGSOC chemotherapeutics. Our results comport with a dualistic origin for HGSOC and suggest the cell-of-origin could influence therapeutic response.\n\nSIGNIFICANCEThe cell-of-origin for high grade serous ovarian carcinoma (HGSOC) has been controversial. By generating novel GEMMs and organoid models with the same oncogenic defects, we demonstrate that HGSOC can originate from either fallopian tube epithelium (FTE) or ovarian surface epithelium (OSE). Importantly, FTE- and OSE-derived tumors differ significantly in biologic properties.

cancer biology↗

bioRxiv2018

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Epigenomic data on transcription factor occupancy and chromatin accessibility can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. However, in many cancers, epigenomic analyses have been limited, and computational methods to infer regulatory networks in tumors typically use expression data alone, or rely on transcription factor (TF) motifs in annotated promoter regions. Here, we develop a novel machine learning strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine cell line chromatin accessibility data with large tumor expression data sets and model the effect of enhancers on transcriptional programs in multiple cancers. We generated a new ATAC-seq data set profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and applied PSIONIC to 723 RNA-seq experiments from ovarian, uterine, and basal breast tumors as well as 96 cell line RNA-seq profiles. Our computational framework enables us to share information across tumors to learn patient-specific inferred TF activities, revealing regulatory differences between and within tumor types. Many of the identified TF regulators were significantly associated with survival outcome in basal breast, uterine serous and endometrioid carcinomas. Moreover, PSIONIC-predicted activity for MTF1 in cell line models correlated with sensitivity to MTF1 inhibition. Therefore computationally dissecting the role of TFs in gynecologic cancers may ultimately advance personalized therapy.

bioinformatics↗

bioRxiv2017

Immune-active microenvironment in small cell carcinoma of the ovary, hypercalcemic type: rationale for immune checkpoint blockade

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a highly aggressive monogenic cancer driven by SMARCA4 mutations. Here, we report responses to anti-PD1 immunotherapy in four patients and characterize the immune landscape of SCCOHT tumors using quantitative immunofluorescence and gene expression profiling. Unexpectedly for a low mutation burden cancer, the majority of the tumors demonstrated PD-L1 expression with strong associated T cell infiltration. PD-L1 expression was detected in both tumor and stromal cells, with macrophages being the most abundant PD-L1 positive cells in some tumors. Transcriptional profiling revealed increased expression of genes related to Th1 and cytotoxic cell function in PD-L1 high tumors, suggesting that PD-L1 acts as a pathway of adaptive immune resistance in SCCOHT. These findings suggest that although SCCOHT are low-mutational-burden tumors, their immunogenic microenvironment that resembles the landscape of tumors that respond well to treatment with PD-1/PD-L1 blockade.

cancer biology↗