Analysis of Prostate Cancer Tumor Microenvironment Identifies Reduced Stromal CD4 Effector T-cell Infiltration in Tumors with Pelvic Nodal Metastasis.

BACKGROUND: Pelvic nodal metastasis in prostate cancer impacts patient outcome negatively. OBJECTIVE: To explore tumor-infiltrating immune cells as a potential predictive tool for regional lymph node (LN) metastasis. DESIGN SETTING AND PARTICIPANTS: We applied multiplex immunofluorescence and targeted transcriptomic analysis on 94 radical prostatectomy specimens in patients with (LN+) or without (LN-) pelvic nodal metastases. Both intraepithelial and stromal infiltrations of immune cells and differentially expressed genes (mRNA and protein levels) were correlated with the nodal status. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The identified CD4 effector cell signature of nodal metastasis was validated in a comparable independent patient cohort of 184 informative cases. Patient outcome analysis and decision curve analysis were performed with the CD4 effector cell density-based signature. RESULTS AND LIMITATIONS: In the discovery cohort, both tumor epithelium and stroma from patients with nodal metastasis had significantly lower infiltration of multiple immune cell types, with stromal CD4 effector cells highlighted as the top candidate marker. Targeted gene expression analysis and confirmatory protein analysis revealed key alteration of extracellular matrix components in tumors with nodal metastasis. Of note, stromal CD4 immune cell density was a significant independent predictor of LN metastasis (odds ratio [OR] = 0.15, p = 0.004), and was further validated as a significant predictor of nodal metastasis in the validation cohort (OR = 0.26, p < 0.001). CONCLUSIONS: Decreased T-cell infiltrates in the primary tumor (particularly CD4 effector cells) are associated with a higher risk of LN metastasis. Future evaluation of CD4-based assays on prostate cancer diagnostic biopsy materials may improve selection of at-risk patients for the treatment of LN metastasis. PATIENT SUMMARY: In this report, we found that cancer showing evidence of cancer metastasis to the lymph nodes tends to have less immune cells present within the tumor. We conclude that the extent of immune cells present within a prostate tumor can help doctors determine the most appropriate treatment plan for individual patients

Identification of cancer hallmarks in patients with non-metastatic colon cancer after surgical resection

Colon cancer is one of the most common cancers in the world, and the therapeutic workflow is dependent on the TNM staging system and the presence of clinical risk factors. However, in the case of patients with non-metastatic disease, evaluating the benefit of adjuvant chemotherapy is a clinical challenge. Radiomics could be seen as a non-invasive novel imaging biomarker able to outline tumor phenotype and to predict patient prognosis by analyzing preoperative medical images. Radiomics might provide decisional support for oncologists with the goal to reduce the number of arbitrary decisions in the emerging era of personalized medicine. To date, much evidence highlights the strengths of radiomics in cancer workup, but several aspects limit the use of radiomics methods as routine. The study aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups—High-risk and No-risk—following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC &gt; 0.8). Stable features were compared between the two groups (T-test or Mann–Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≤ ICC &lt;  0.92). Among these, 28 features were significantly different between the two groups (p &lt; 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk diseas

The immune microenvironment in mantle cell lymphoma : Targeted liquid and spatial proteomic analyses

The complex interplay of the tumour and immune cells affects tumour growth, progression, and response to treatment. Restorationof effective immune response forms the basis of onco-immunology, which further enabled the development of immunotherapy. Inthe era of precision medicine, pin-pointing patient biological heterogeneity especially in relation to patient-specific immunemicroenvironment is a necessity for the discovery of novel biomarkers and for development of patient stratification tools for targetedtherapeutics. Mantle cell lymphoma (MCL) is a rare and aggressive subtype of B-cell lymphoma with poor survival and high relapserates. Previous investigations of MCL have largely focused on the tumour itself and explorations of the immune microenvironmenthave been limited. This thesis and the included five papers, investigates multiple aspects of the immune microenvironment withrespect to proteomic analysis performed on tissue and liquid biopsies of diagnostic and relapsed/refractory (R/R) MCL cohorts.Analyses based on liquid biopsies (serum) in particular are relevant for aggressive cases such as in relapse, where invasiveprocedures for extracting tissues is not recommended. Thus, paper I-II probes the possibility of using serum for treatment andoutcome-associated biomarker discovery in R/R MCL, using a targeted affinity-based protein microarray platform quantifyingimmune-regulatory and tumor-secretory proteins in sera. Analysis performed in paper I using pre-treatment samples, identifies 11-plex biomarker signature (RIS – relapsed immune signature) associated with overall survival. Further integration of RIS with mantlecell lymphoma international prognostic index (MIPI) led to the development of MIPIris index for the stratification of R/R MCL intothree risk groups. Moreover, longitudinal analysis can be important in understanding how patient respond to treatment and thiscan further guide therapeutic interventions. Thus, paper II is a follow-up study wherein longitudinal analyses was performed onpaired samples collected at pre-treatment (baseline) and after three months of chemo-immunotherapy (on-treatment). We showhow genetic aberrations can influence systemic profiles and thus integrating genetic information can be crucial for treatmentselection. Furthermore, we observe that the inter-patient heterogeneity associated with absolute values can be circumvented byusing velocity of change to capture general changes over time in groups of patients. Thus, using velocity of change in serumproteins between pre- and on-treatment samples identified response biomarkers associated with minimal residual disease andprogression. While exploratory analysis using high dimensional omics-based data can be important for accelerating discovery,translating such information for clinical utility is a necessity. Thus, in paper III, we show how serum quantification can be usedcomplementary tissue-identified prognostic biomarkers and this can enable faster clinical implementation. Presence of CD163+M2-like macrophages has shown to be associated with poor outcome in MCL tissues. We show that higher expression of sCD163levels in sera quantified using ELISA, is also associated with poor outcome in diagnostic and relapsed MCL. Furthermore, wesuggest a cut-off for sCD163 levels that can be used for clinical utility. Further exploration of the dynamic interplay of tumourimmunemicroenvironment is now possible using spatial resolved omics for tissue-based analysis. Thus, in paper IV and V, weanalyse cell-type specific proteomic data collected from tumour and immune cells using GeoMx™ digital spatial profiler. In paperIV, we show that presence as well as spatial localization of CD163+ macrophage with respect to tumour regions impactsmacrophage phenotypic profiles. Further modulation in the profile of surrounding tumour and T-cells is observed whenmacrophages are present in the vicinity. Based on this analysis, we suggest MAPK pathway as a potential therapeutic target intumours with CD163+ macrophages. Immune composition can be defined not just by the type of cells, but also with respect tofrequency and spatial localization and this is explored in paper V with respect to T-cell subtypes. Thus, in paper V, we optimizeda workflow of multiplexed immunofluorescence image segmentation that allowed us to extract cell metrics for four subtypes ofCD3+ T-cells. Using this data, we show that higher infiltration of T-cells is associated with a positive outcome in MCL. Moreover,by combining image derived metrics to cell specific spatial omics data, we were able to identify immunosuppressivemicroenvironment associated with highly infiltrated tumours and suggests new potential targets of immunotherapy with respect toIDO1, GITR and STING. In conclusion, this thesis explores systemic and tumor-associated immune microenvironment in MCL, fordefining patient heterogeneity, developing methods of patient stratification and for identifying novel and actionable biomarkers

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming yearsThis research was partially funded by DTS17/00138 (Instituto de Salud Carlos III) and ED431F 2017/04 project (GAIN-Xunta de Galicia)S