Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategies to improve clinical outcomes. Raman spectroscopy (RS) can monitor post-irradiation biomolecular changes and signatures of radiation response in tumour cells in a label-free manner. Convolutional Neural Networks (CNN) perform feature extraction directly from data in an end-to-end learning manner, with high classification performance. Furthermore, recently developed CNN explainability techniques help visualize the critical discriminative features captured by the model. In this work, a CNN is developed to characterize tumour response to radiotherapy based on its degree of radioresistance. The model was trained to classify Raman spectra of three human tumour cell lines as radiosensitive (LNCaP) or radioresistant (MCF7, H460) over a range of treatment doses and data collection time points. Additionally, a method based on Gradient-Weighted Class Activation Mapping (Grad-CAM) was used to determine response-specific salient Raman peaks influencing the CNN predictions. The CNN effectively classified the cell spectra, with accuracy, sensitivity, specificity, and F1 score exceeding 99.8%. Grad-CAM heatmaps of H460 and MCF7 cell spectra (radioresistant) exhibited high contributions from Raman bands tentatively assigned to glycogen, amino acids, and nucleic acids. Conversely, heatmaps of LNCaP cells (radiosensitive) revealed activations at lipid and phospholipid bands. Finally, Grad-CAM variable importance scores were derived for glycogen, asparagine, and phosphatidylcholine, and we show that their trends over cell line, dose, and acquisition time agreed with previously established models. Thus, the CNN can accurately detect biomolecular differences in the Raman spectra of tumour cells of varying radiosensitivity without requiring manual feature extraction. Finally, Grad-CAM may help identify metabolic signatures associated with the observed categories, offering the potential for automated clinical tumour radiation response characterization.

Ars2 links the nuclear cap-binding complex to RNA interference and cell proliferation.

Here we identify a component of the nuclear RNA cap-binding complex (CBC), Ars2, that is important for miRNA biogenesis and critical for cell proliferation. Unlike other components of the CBC, Ars2 expression is linked to the proliferative state of the cell. Deletion of Ars2 is developmentally lethal, and deletion in adult mice led to bone marrow failure whereas parenchymal organs composed of nonproliferating cells were unaffected. Depletion of Ars2 or CBP80 from proliferating cells impaired miRNA-mediated repression and led to alterations in primary miRNA processing in the nucleus. Ars2 depletion also reduced the levels of several miRNAs, including miR-21, let-7, and miR-155, that are implicated in cellular transformation. These findings provide evidence for a role for Ars2 in RNA interference regulation during cell proliferation.

Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues.

Recent advancements in anatomical imaging of tumours as treatment targets have led to improvements in RT. However, it is unlikely that improved anatomical imaging alone will be the sole driver for new advances in personalised RT. Biochemically based radiobiological information is likely to be required for next-generation improvements in the personalisation of radiotherapy dose prescriptions to individual patients. In this paper, we use Raman spectroscopy (RS), an optical technique, to monitor individual biochemical response to radiation within a tumour microenvironment. We spatially correlate individual biochemical responses to augmentatively derived hypoxic maps within the tumour microenvironment. Furthermore, we pair RS with a data analytical framework combining (i) group and basis restricted non-negative matrix factorization (GBR-NMF), (ii) a random forest (RF) classifier, (iii) and a feature metric importance calculation method, Shapley Additive exPlanations (SHAP), in order to ascertain the relative importance of individual biochemicals in describing the overall biological response as observed with RS. The current study found that the GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators (e.g., glycogen, lipids, DNA, amino acids) in H460 human lung cancer cells and H460 xenografts. Correlations between the hypoxic regions and Raman chemical biomarkers (e.g., glycogen, alanine, and arginine) were also identified in H460 xenografts. To summarize, GBR-NMF-RF-SHAP combined with RS can be applied to monitor the RT-induced biochemical response within cellular and tissue environments. Individual biochemicals were identified that (i) contributed to overall biological response to radiation, and (ii) spatially correlated with hypoxic regions of the tumour. RS combined with our analytical pipeline shows promise for further understanding of individual biochemical dynamics in radiation response for use in cancer therapy.

Pan-specific and partially selective dye-labeled peptidic inhibitors of the polycomb paralog proteins.

Epigenetic regulation of gene expression is in part controlled by post-translational modifications on histone proteins. Histone methylation is a key epigenetic mark that controls gene transcription and repression. There are five human polycomb paralog proteins (Cbx2/4/6/7/8) that use their chromodomains to recognize trimethylated lysine 27 on histone 3 (H3K27me3). Recognition of the methyllysine side chain is achieved through multiple cation-pi interactions within an 'aromatic cage' motif. Despite high structural similarity within the chromodomains of this protein family, they each have unique functional roles and are linked to different cancers. Selective inhibition of different CBX proteins is desirable for both fundamental studies and potential therapeutic applications. We report here on a series of peptidic inhibitors that target certain polycomb paralogs. We have identified peptidic scaffolds with sub-micromolar potency, and will report examples that are pan-specific and that are partially selective for individual members within the family. These results highlight important structure-activity relationships that allow for differential binding to be achieved through interactions outside of the methyllysine-binding aromatic cage motif.

Raman spectroscopy detects metabolic signatures of radiation response and hypoxic fluctuations in non-small cell lung cancer.

BACKGROUND: Radiation therapy is a standard form of treating non-small cell lung cancer, however, local recurrence is a major issue with this type of treatment. A better understanding of the metabolic response to radiation therapy may provide insight into improved approaches for local tumour control. Cyclic hypoxia is a well-established determinant that influences radiation response, though its impact on other metabolic pathways that control radiosensitivity remains unclear. METHODS: We used an established Raman spectroscopic (RS) technique in combination with immunofluorescence staining to measure radiation-induced metabolic responses in human non-small cell lung cancer (NSCLC) tumour xenografts. Tumours were established in NOD.CB17-Prkdcscid/J mice, and were exposed to radiation doses of 15 Gy or left untreated. Tumours were harvested at 2 h, 1, 3 and 10 days post irradiation. RESULTS: We report that xenografted NSCLC tumours demonstrate rapid and stable metabolic changes, following exposure to 15 Gy radiation doses, which can be measured by RS and are dictated by the extent of local tissue oxygenation. In particular, fluctuations in tissue glycogen content were observed as early as 2 h and as late as 10 days post irradiation. Metabolically, this signature was correlated to the extent of tumour regression. Immunofluorescence staining for γ-H2AX, pimonidazole and carbonic anhydrase IX (CAIX) correlated with RS-identified metabolic changes in hypoxia and reoxygenation following radiation exposure. CONCLUSION: Our results indicate that RS can identify sequential changes in hypoxia and tumour reoxygenation in NSCLC, that play crucial roles in radiosensitivity.

Breast cancer subtype specific biochemical responses to radiation.

External beam radiotherapy is a common form of treatment for breast cancer. Among patients and across different breast cancer subtypes, the response to radiation is heterogeneous. Radiation-induced biochemical changes were examined by Raman spectroscopy using cell lines that represent a spectrum of human breast cancer. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) revealed unique Raman spectral features in the HER2 and Ki67 subtype. The changes in Raman spectral profiles to different doses of radiation (0-50 Gy) included variations in the levels of proteins, lipids, nucleic acids and glycogen. Importantly, the differences in radiation-induced changes on the normal breast epithelial cell line MCF10A could be discriminated within and across the various breast tumor cell lines. These results demonstrate a novel approach to uncover differences between breast cancer cell subtypes and surrounding normal tissues by their biochemical variations in response to radiation.

Survival of effector CD8+ T cells during influenza infection is dependent on autophagy.

The activation and expansion of effector CD8(+) T cells are essential for controlling viral infections and tumor surveillance. During an immune response, T cells encounter extrinsic and intrinsic factors, including oxidative stress, nutrient availability, and inflammation, that can modulate their capacity to activate, proliferate, and survive. The dependency of T cells on autophagy for in vitro and in vivo activation, expansion, and memory remains unclear. Moreover, the specific signals and mechanisms that activate autophagy in T effector cells and their survival are not known. In this study, we generated a novel inducible autophagy knockout mouse to study T cell effector responses during the course of a virus infection. In response to influenza infection, Atg5(-/-) CD8(+) T cells had a decreased capacity to reach the peak effector response and were unable to maintain cell viability during the effector phase. As a consequence of Atg5 deletion and the impairment in effector-to-memory cell survival, mice fail to mount a memory response following a secondary challenge. We found that Atg5(-/-) effector CD8(+) T cells upregulated p53, a transcriptional state that was concomitant with widespread hypoxia in lymphoid tissues of infected mice. The onset of p53 activation was concurrent with higher levels of reactive oxygen species (ROS) that resulted in ROS-dependent apoptotic cell death, a fate that could be rescued by treating with the ROS scavenger N-acetylcysteine. Collectively, these results demonstrate that effector CD8(+) T cells require autophagy to suppress cell death and maintain survival in response to a viral infection.

Autophagy inhibition in cancer therapy: metabolic considerations for antitumor immunity.

Tumors and the immune system are intertwined in a competition where tilting the fine balance between tumor-specific immunity and tolerance can ultimately decide the fate of the host. Defensive and suppressive immunological responses to cancer are exquisitely sensitive to metabolic features of rapidly growing tumors, such as hypoxia, low nutrient availability, and aberrant growth factor signaling. As a result, clinical therapies impacting these properties change the in situ antitumor immune response by virtue of disrupting the tumor environment. To compensate for disruptions in cellular metabolism, cells activate autophagy to promote survival. On the basis of this notion, strategies designed to block autophagy in tumor cells are currently being tested in several human clinical trials. However, therapies that impair tumor metabolism must also take into account their effect on lymphocytes activated in the immune response to cancer. Given that a strong antitumor immune response is a positive prognostic factor in overall patient survival, identifying ways to block essential processes in tumor cells and suppressive immune cells while promoting those that are important for a robust immune response are of critical importance. Herein, we review the effects of anti-cancer agents that impact metabolism administered concurrently with autophagy inhibitors on immune cells and consider the implications for patient response to therapy.

Metabolic engineering for optimized CAR-T cell therapy.

The broad effectiveness of T cell-based therapy for treating solid tumour cancers remains limited. This is partly due to the growing appreciation that immune cells must inhabit and traverse a metabolically demanding tumour environment. Accordingly, recent efforts have centred on using genome-editing technologies to augment T cell-mediated cytotoxicity by manipulating specific metabolic genes. However, solid tumours exhibit numerous characteristics restricting immune cell-mediated cytotoxicity, implying a need for metabolic engineering at the pathway level rather than single gene targets. This emerging concept has yet to be put into clinical practice as many questions concerning the complex interplay between metabolic networks and T cell function remain unsolved. This Perspective will highlight key foundational studies that examine the relevant metabolic pathways required for effective T cell cytotoxicity and persistence in the human tumour microenvironment, feasible strategies for metabolic engineering to increase the efficiency of chimeric antigen receptor T cell-based approaches, and the challenges lying ahead for clinical implementation.

Mass spectrometry imaging methods for visualizing tumor heterogeneity.

Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.

The biology of cancer: metabolic reprogramming fuels cell growth and proliferation.

Cell proliferation requires nutrients, energy, and biosynthetic activity to duplicate all macromolecular components during each passage through the cell cycle. It is therefore not surprising that metabolic activities in proliferating cells are fundamentally different from those in nonproliferating cells. This review examines the idea that several core fluxes, including aerobic glycolysis, de novo lipid biosynthesis, and glutamine-dependent anaplerosis, form a stereotyped platform supporting proliferation of diverse cell types. We also consider regulation of these fluxes by cellular mediators of signal transduction and gene expression, including the phosphatidylinositol 3-kinase (PI3K)/Akt/mTOR system, hypoxia-inducible factor 1 (HIF-1), and Myc, during physiologic cell proliferation and tumorigenesis.

Quantification of a proteotypic peptide from protein C inhibitor by liquid chromatography-free SISCAPA-MALDI mass spectrometry: application to identification of recurrence of prostate cancer.

BACKGROUND: Biomarker validation remains one of the most challenging constraints to the development of new diagnostic assays. To facilitate biomarker validation, we previously developed a chromatography-free stable isotope standards and capture by antipeptide antibodies (SISCAPA)-MALDI assay allowing rapid, high-throughput quantification of protein analytes in large sample sets. Here we applied this assay to the measurement of a surrogate proteotypic peptide from protein C inhibitor (PCI) in sera from patients with prostate cancer. METHODS: A 2-plex SISCAPA-MALDI assay for quantification of proteotypic peptides from PCI and soluble transferrin receptor (sTfR) was used to measure these peptides in 159 trypsin-digested sera collected from 51 patients with prostate cancer. These patients had been treated with radiation with or without neoadjuvant androgen deprivation. RESULTS: Patients who experienced biochemical recurrence of prostate cancer showed decreased serum concentrations of the PCI peptide analyte within 18 months of treatment. The PCI peptide concentrations remained increased in the sera of patients who did not experience cancer recurrence. Prostate-specific antigen concentrations had no predictive value during the same time period. CONCLUSIONS: The high-throughput, liquid chromatography-free SISCAPA-MALDI assay is capable of rapid quantification of proteotypic PCI and sTfR peptide analytes in complex serum samples. Decreased serum concentrations of the PCI peptide were found to be related to recurrence of prostate cancer in patients treated with radiation with or without hormone therapy. However, a larger cohort of patients will be required for unequivocal validation of the PCI peptide as a biomarker for clinical use.

The autophagy protein LC3A correlates with hypoxia and is a prognostic marker of patient survival in clear cell ovarian cancer.

Clear cell ovarian cancer histotypes exhibit metabolic features associated with resistance to hypoxia and glucose deprivation-induced cell death. This metabolic characteristic suggests that clear cell ovarian cancers activate survival mechanisms not typical of other epithelial ovarian cancers. Here we demonstrate that microtubule-associated protein 1 light chain 3A (LC3A), a marker of autophagy, is related to hypoxia and poor prognosis in clear cell ovarian cancer. In 485 ovarian tumours, we found that LC3A was significantly associated with poor progression-free (p = 0.0232), disease-specific (p = 0.0011) and overall patient survival (p = 0.0013) in clear cell ovarian cancer patients, but not in other subtypes examined. LC3A was an independent prognostic marker of reduced disease-specific [hazard ratio (HR): 2.55 (95% CI 1.21-5.37); p = 0.014] and overall survival [HR: 1.95 (95% CI 1.00-3.77); p = 0.049] in patients with clear cell ovarian carcinoma. We also found a strong link between autophagy and hypoxia as LC3A staining revealed a significant positive association with the hypoxia-related proteins carbonic anhydrase-IX and HIF-1α. The functional link between hypoxia and autophagy was demonstrated using clear cell and high-grade serous cell lines that were subjected to hypoxia or hypoxia + glucose deprivation. Clear cell carcinoma lines displayed greater autophagy induction and were subsequently more sensitive to inhibition of autophagy under hypoxia compared to the high-grade serous lines. Together, our findings indicate that hypoxia-induced autophagy may be crucial to the clinical pathology of clear cell ovarian cancer and is a potential explanation for histological subtype differences in patient disease progression and outcomes.

Strategies for uncovering stable isotope tracing patterns between cell populations.

Despite practical complexities, isotope tracing studies in humans are becoming increasingly feasible. However, several technological challenges need to be addressed in order to take full advantage of human tracing studies. First, absolute metabolic flux measurements in mice are not so easily applied to human models, given that tissue resection is restricted to a single surgical time point. Second, isotope tracing has yet to be employed to detect metabolic differences between cells types in vivo. Here, we discuss the current models and propose an alternative, liquid tumor environment, that could overcome these limitations. Furthermore, we highlight current strategies used to maintain isotopolog enrichment following cell isolation techniques to facilitate cell-type-specific analysis.

Decision Aids for Patients With Head and Neck Cancer: Qualitative Elicitation of Design Recommendations From Patient End Users.

BACKGROUND: Patients with head and neck cancer (HNC) carry a clinically significant symptom burden, have alterations in function (eg, impaired ability to chew, swallow, and talk), and decrease in quality of life. Furthermore, treatment impacts social activities and interactions as patients report reduced sexuality and shoulder the highest rates of depression across cancer types. Patients suffer undue anxiety because they find the treatment incomprehensible, which is partially a function of limited, understandable information. Patients' perceptions of having obtained adequate information prior to and during treatment are predictive of positive outcomes. Providing patient-centered decision support and utilizing visual images may increase understanding of treatment options and associated risks to improve satisfaction with their decision and consultation, while reducing decisional conflict. OBJECTIVE: This study aims to gather requirements from survivors of HNC on the utility of key visual components to be used in the design of an electronic decision aid (eDA) to assist with decision-making on treatment options. METHODS: Informed by a scoping review on eDAs for patients with HNC, screens and visualizations for an eDA were created and then presented to 12 survivors of HNC for feedback on their utility, features, and further requirements. The semistructured interviews were video-recorded and thematically analyzed to inform co-design recommendations. RESULTS: A total of 9 themes were organized into 2 categories. The first category, eDAs and decision support, included 3 themes: familiarity with DAs, support of concept, and versatility of the prototype. The second category, evaluation of mock-up, contained 6 themes: reaction to the screens and visualizations, favorite features, complexity, preference for customizability, presentation device, and suggestions for improvement. CONCLUSIONS: All participants felt an eDA, used in the presence of their oncologist, would support a more thorough and transparent explanation of treatment or augment the quality of education received. Participants liked the simple design of the mock-ups they were shown but, ultimately, desired customizability to adapt the eDA to their individual information needs. This research highlights the value of user-centered design, rooted in acceptability and utility, in medical health informatics, recognizing cancer survivors as the ultimate knowledge holders. This research highlights the value of incorporating visuals into technology-based innovations to engage all patients in treatment decisions.

Assessing the Immune Regulatory Role of Metabolites In Vitro.

One method of immune evasion that cancer cells employ is the secretion of immune regulatory metabolites into the tumor microenvironment (TME). These metabolites can promote immunosuppressive cell subsets, while inhibiting key tumor-killing subsets, such as T cells. Thus, the identification of these metabolites may help develop methods for improving cell-based therapy. However, after identifying a potential immune regulatory metabolite, it is crucial to assess the impacts of the metabolite on T cell immunobiology. In this chapter, we describe an in vitro method of testing and analyzing the influence of a specific metabolite on T cell proliferation and function.

Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted Non-Negative Matrix Factorization.

Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, on the condition that all biochemicals in the mixture were included as bases chemicals in the model. We also report that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely due to similarity in the individual bases spectra.

Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.

Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.

CD8 + T cell infiltration is associated with improved survival and negatively correlates with hypoxia in clear cell ovarian cancer.

Unlike other histological types of epithelial ovarian carcinoma, clear cell ovarian carcinoma (CCOC) has poor response to therapy. In many other carcinomas, expression of the hypoxia-related enzyme Carbonic anhydrase IX (CAIX) by cancer cells is associated with poor prognosis, while the presence of CD8 + tumor-infiltrating lymphocytes (TIL) is positively prognostic. We employed [18F]EF5-PET/CT imaging, transcriptome profiling, and spatially-resolved histological analysis to evaluate relationships between CAIX, CD8, and survival in CCOC. Tissue microarrays (TMAs) were evaluated for 218 cases in the Canadian COEUR study. Non-spatial relationships between CAIX and CD8 were investigated using Spearman rank correlation, negative binomial regression and gene set enrichment analysis. Spatial relationships at the cell level were investigated using the cross K-function. Survival analysis was used to assess the relationship of CAIX and CD8 with patient survival for 154 cases. CD8 + T cell infiltration positively predicted survival with estimated hazard ratio 0.974 (95% CI 0.950, 1000). The negative binomial regression analysis found a strong TMA effect (p-value < 0.0001). It also indicated a negative association between CD8 and CAIX overall (p-value = 0.0171) and in stroma (p-value = 0.0050) but not in tumor (p-value = 0.173). Examination of the spatial association between the locations of CD8 + T cells and CAIX cells found a significant amount of heterogeneity in the first TMA, while in the second TMA there was a clear signal indicating negative spatial association in stromal regions. These results suggest that hypoxia may contribute to immune exclusion, primarily mediated by effects in stroma.