Surveillance of response to PARP inhibitors using characterized Extracellular Vesicles

https://openworks.mdanderson.org/sumexp22/1019/thumbnail.jp

Tumour microenvironment in serous ovarian cancer

Ovarian cancer is the seventh most common malignancy in women worldwide and the most lethal gynaecological malignancy in developed countries. The epithelial subtype is divided in five main histologic groups, of which the high-grade serous is the most common subtype. Advanced stage at diagnosis, poor prognosis and high incidence of resistance to therapy constitute the most important challenges for patients with ovarian cancer. The search to identify new prognostic and predictive markers represents one of the major goals in the research field of high-grade serous ovarian cancer (HGSOC). During tumour development and progression, the ovarian stroma, constituted by blood vessels, fibroblasts, smooth muscle cells and connective tissue, sustains cancer cells through synergistic paracrine communications. This thesis aimed at studying the components of tumour microenvironment in serous ovarian cancer, especially HGSOC, and their possible association with survival and response to therapy. We identified PDGFRβ positive stroma fibroblasts and perivascular cells as a determinant of poor survival in serous ovarian cancer. When we characterized PDGFRβ positive stroma and vasculature of ovarian cancer, compared to renal and colorectal cancer, we found both similarities and differences. We also studied the interaction between cancer-associated fibroblasts and immune cells and noted an inhibitory effect of FAP positive fibroblasts in patients with high tumour infiltration of CD8 positive T cells on response to platinum-based treatment in a population of HGSOC patients. On the same population, we studied the macrophage profile and discovered a correlation of two distinct subtypes of macrophages (CD11c and CD80 positive) in specific tumour localizations, with overall and progression-free survival respectively, indicating independent effects of these subsets on natural course of the disease and response to treatment. In summary, our research identified a number of tumour stroma-related measurable features associated with survival and response to treatment. Our findings support continued analyses of ovarian cancer tumour microenvironment in order to discover and develop new prognostic and predictive tools, to improve the clinical outcome in ovarian cancer

In situ profiling reveals metabolic alterations in the tumor microenvironment of ovarian cancer after chemotherapy

In this study, we investigated the metabolic alterations associated with clinical response to chemotherapy in patients with ovarian cancer. Pre- and post-neoadjuvant chemotherapy (NACT) tissues from patients with high-grade serous ovarian cancer (HGSC) who had poor response (PR) or excellent response (ER) to NACT were examined. Desorption electrospray ionization mass spectrometry (DESI-MS) was performed on sections of HGSC tissues collected according to a rigorous laparoscopic triage algorithm. Quantitative MS-based proteomics and phosphoproteomics were performed on a subgroup of pre-NACT samples. Highly abundant metabolites in the pre-NACT PR tumors were related to pyrimidine metabolism in the epithelial regions and oxygen-dependent proline hydroxylation of hypoxia-inducible factor alpha in the stromal regions. Metabolites more abundant in the epithelial regions of post-NACT PR tumors were involved in the metabolism of nucleotides, and metabolites more abundant in the stromal regions of post-NACT PR tumors were related to aspartate and asparagine metabolism, phenylalanine and tyrosine metabolism, nucleotide biosynthesis, and the urea cycle. A predictive model built on ions with differential abundances allowed the classification of patients\u27 tumor responses as ER or PR with 75% accuracy (10-fold cross-validation ridge regression model). These findings offer new insights related to differential responses to chemotherapy and could lead to novel actionable targets

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming, subjective, and requires extensive expertise. This paper presents the first label-free, quantitative, and automated histological recognition of ovarian tissue subtypes using a new MIRSI technique. This technique, called optical photothermal infrared (O-PTIR) imaging, provides a 10X enhancement in spatial resolution relative to prior instruments. It enables sub-cellular spectroscopic investigation of tissue at biochemically important fingerprint wavelengths. We demonstrate that enhanced resolution of sub-cellular features, combined with spectroscopic information, enables reliable classification of ovarian cell subtypes achieving a classification accuracy of 0.98. Moreover, we present statistically robust validation from 74 patient samples with over 60 million data points. We show that sub-cellular resolution from five wavenumbers is sufficient to outperform state-of-the-art diffraction-limited techniques from up to 235 wavenumbers. We also propose two quantitative biomarkers based on the relative quantities of epithelium and stroma that exhibits efficacy in early cancer diagnosis. This paper demonstrates that combining deep learning with intrinsic biochemical MIRSI measurements enables quantitative evaluation of cancerous tissue, improving the rigor and reproducibility of histopathology

Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. Our method significantly accelerates data collection by capturing a combination of high-resolution and interleaved, lower-resolution infrared band images and applying computational techniques for data interpolation. We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms. This method enables the reconstruction of high-quality, high-resolution images from undersampled datasets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by ROC curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%

Adaptive RSK-EphA2-GPRC5A signaling switch triggers chemotherapy resistance in ovarian cancer

Metastatic cancers commonly activate adaptive chemotherapy resistance, attributed to both microenvironment-dependent phenotypic plasticity and genetic characteristics of cancer cells. However, the contribution of chemotherapy itself to the non-genetic resistance mechanisms was long neglected. Using high-grade serous ovarian cancer (HGSC) patient material and cell lines, we describe here an unexpectedly robust cisplatin and carboplatin chemotherapy-induced ERK1/2-RSK1/2-EphA2-GPRC5A signaling switch associated with cancer cell intrinsic and acquired chemoresistance. Mechanistically, pharmacological inhibition or knockdown of RSK1/2 prevented oncogenic EphA2-S897 phosphorylation and EphA2-GPRC5A co-regulation, thereby facilitating a signaling shift to the canonical tumor-suppressive tyrosine phosphorylation and consequent downregulation of EphA2. In combination with platinum, RSK inhibitors effectively sensitized even the most platinum-resistant EphA2(high), GPRC5A(high) cells to the therapy-induced apoptosis. In HGSC patient tumors, this orphan receptor GPRC5A was expressed exclusively in cancer cells and associated with chemotherapy resistance and poor survival. Our results reveal a kinase signaling pathway uniquely activated by platinum to elicit adaptive resistance. They further identify GPRC5A as a marker for abysmal HGSC outcome and putative vulnerability of the chemo-resistant cells to RSK1/2-EphA2-pS897 pathway inhibition.Peer reviewe

Survival-associated heterogeneity of marker-defined perivascular cells in colorectal cancer

Perivascular cells (PC) were recently implied as regulators of metastasis and immune cell activity. Perivascular heterogeneity in clinical samples, and associations with other tumor features and outcome, remain largely unknown. Here we report a novel method for digital quantitative analyses of vessel characteristics and PC, which was applied to two collections of human metastatic colorectal cancer (mCRC). Initial analyses identified marker-defined subsets of PC, including cells expressing PDGFR-beta or alpha-SMA or both markers. PC subsets were largely independently expressed in a manner unrelated to vessel density and size. Association studies implied specific oncogenic mutations in malignant cells as determinants of PC status. Semi-quantitative and digital-image-analyses-based scoring of the NORDIC-VII cohort identified significant associations between low expression of perivascular PDGFR-alpha and -beta and shorter overall survival. Analyses of the SPCRC cohort confirmed these findings. Perivascular PDGFR-alpha and -beta remained independent factors for survival in multivariate analyses. Overall, our study identified host vasculature and oncogenic status as determinants of tumor perivascular features. Perivascular PDGFR-alpha and -beta were identified as novel independent markers predicting survival in mCRC. The novel methodology should be suitable for similar analyses in other tumor collections

The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types

Background: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed.Methods: Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns.Findings: The stroma fraction was highly variable within and across the tumour types, with kidney cancer showing the lowest and pancreato-biliary type periampullary cancer showing the highest stroma proportion (median 19% and 73% respectively). Adjusted Cox regression models revealed both positive (pancreato-biliary type periampullary cancer and oestrogen negative breast cancer, HR(95%CI)=0.56(0.34-0.92) and HR (95%CI)=0.41(0.17-0.98) respectively) and negative (intestinal type periampullary cancer, HR(95%CI)=3.59 (1.49-8.62)) associations of the tumour stroma fraction with survival.Interpretation: Our study provides an objective quantification of the tumour stroma fraction across major types of solid cancer. Findings strongly argue against the commonly promoted view of a general associations between high stroma abundance and poor prognosis. The results also suggest that full exploitation of the prognostic potential of tumour stroma requires analyses that go beyond determination of stroma abundance.</p

Combined inhibitory effect of formestane and herceptin on a subpopulation of CD44+/CD24low breast cancer cells.

In breast cancer, stromal cells surrounding cancer epithelial cells can influence phenotype by producing paracrine factors. Among many mediators of epithelial-stromal interactions, aromatase activity is perhaps one of the best studied. Clinical data suggest that estrogen-independent signaling leads to increased proliferation even during therapy with aromatase inhibitors (AIs). Molecular mechanism of crosstalk between the estrogen receptor (ER) and the epidermal growth factor receptor (HER) family have been implicated in resistance to endocrine therapy, but this interaction is unclear. The ability of aromatase to induce estradiol biosynthesis provides a molecular rationale to combine agents that target aromatase activity and the HER pathway. We targeted stromal-epithelial interactions using formestane, which exerts antiaromatase activity, combined with the monoclonal anti-HER2 antibody herceptin, in a subpopulation of CD44+/CD24low cells sorted from epithelial-mesenchymal co-cultures of breast cancer tissues. The growth inhibition was respectively 16% (P < 0.01) in the response to herceptin, 25% to formestane (P < 0.01), and 50% (P < 0.001) with the combination of the two drugs, suggesting that herceptin cooperates with formestane-induced inhibition of aromatase and this effect could be mediated through HER family receptors. In cells which expressed ERalpha, formestane/herceptin combination suppressed the mRNA expression of aromatase and HER2 and decreased cyclin D1 expression. These results show that combination therapies involving AIs and anti-HER2 can be efficacious for the treatment of cancer in experimental models and suggest that subtyping breast tumors gives useful information about response to treatment