Mosaizismo epigenetikoaren kuantifikazioa: protokoloaren diseinua

[EUS] Lan honen helburua da DMRen metilazioaren galera partziala duten PHP1B pazienteen alelo bakoitzeko CpG posizio bakoitzaren metilazio maila neurtzea ahalbidetzen duen metodo azkar, erraz eta merke bat diseinatzea, garatzea eta balioztatzea

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression

Deep learning; Immunotherapy; Solid tumorsAprenentatge profund; Immunoteràpia; Tumors sòlidsAprendizaje profundo; Inmunoterapia; Tumores sólidosProgrammed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1–stained slides from the non–small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1–2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96–2.2), P = 0.082] and CPS [HR: 1.2 (0.79–1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity. Significance: The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.J.N. Kather is supported by the German Federal Ministry of Health (DEEP LIVER, ZMVI1-2520DAT111) and the Max-Eder-Programme of the German Cancer Aid (grant no. 70113864), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A; TANGERINE, 01KT2302 through ERA-NET Transcan), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (Transplant.KI, 01VSF21048) the European Union's Horizon Europe and innovation programme (ODELIA, 101057091; GENIAL, 101096312) and the National Institute for Health and Care Research (NIHR, NIHR213331) Leeds Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. R. Perez-Lopez is supported by LaCaixa Foundation, a CRIS Foundation Talent Award (TALENT19-05), the FERO Foundation, the Instituto de Salud Carlos III-Investigacion en Salud (PI18/01395 and PI21/01019) and the Prostate Cancer Foundation (18YOUN19). M. Ligero is supported by the PERIS PIF-Salut Grant. As per the ICMJE guidelines of April 2023, we hereby disclose that the following artificial intelligence tools were used to write this article: ChatGPT-4 for checking and correcting spelling and grammar

Performance of 16S Metagenomic Profiling in Formalin-Fixed Paraffin-Embedded versus Fresh-Frozen Colorectal Cancer Tissues

Secuenciación del gen 16S; Cáncer colorrectal; MicrobiomaSeqüenciació del gen 16S; Càncer colorectal; Microbioma16S gene sequencing; Colorectal cancer; MicrobiomeFormalin-fixed, paraffin-embedded (FFPE) tissues represent the most widely available clinical material to study colorectal cancer (CRC). However, the accuracy and clinical validity of FFPE microbiome profiling in CRC is uncertain. Here, we compared the microbial composition of 10 paired fresh-frozen (FF) and FFPE CRC tissues using 16S rRNA sequencing and RNA-ISH. Both sample types showed different microbial diversity and composition. FF samples were enriched in archaea and representative CRC-associated bacteria, such as Firmicutes, Bacteroidetes and Fusobacteria. Conversely, FFPE samples were mainly enriched in typical contaminants, such as Sphingomonadales and Rhodobacterales. RNA-ISH in FFPE tissues confirmed the presence of CRC-associated bacteria, such as Fusobacterium and Bacteroides, as well as Propionibacterium allowing discrimination between tumor-associated and contaminant taxa. An internal quality index showed that the degree of similarity within sample pairs inversely correlated with the dominance of contaminant taxa. Given the importance of FFPE specimens for larger studies in human cancer genomics, our findings may provide useful indications on potential confounding factors to consider for accurate and reproducible metagenomics analyses.This project has received funding from “la Caixa” Foundation under the grant agreement LCF/PR/CE07/5061000, the Fundación Mutua Madrileña [MMADRILEÑA/PREMI/2020CCAA_ NUCIFORO], the Instituto de Salud Carlos III [PI20/00889], and Grifols

Association of CD2AP neuronal deposits with Braak neurofibrillary stage in Alzheimer’s disease

Alzheimer; CD2AP; Enfermedad de PickAlzheimer's disease; CD2AP; Pick's diseaseAlzheimer; CD2AP; Malaltia de PickGenome-wide association studies have described several genes as genetic susceptibility loci for Alzheimer's disease (AD). Among them, CD2AP encodes CD2-associated protein, a scaffold protein implicated in dynamic actin remodeling and membrane trafficking during endocytosis and cytokinesis. Although a clear link between CD2AP defects and glomerular pathology has been described, little is known about the function of CD2AP in the brain. The aim of this study was to analyze the distribution of CD2AP in the AD brain and its potential associations with tau aggregation and β-amyloid (Aβ) deposition. First, we performed immunohistochemical analysis of CD2AP expression in brain tissue from AD patients and controls (N = 60). Our results showed granular CD2AP immunoreactivity in the human brain endothelium in all samples. In AD cases, no CD2AP was found to be associated with Aβ deposits in vessels or parenchymal plaques. CD2AP neuronal inclusions similar to neurofibrillary tangles (NFT) and neuropil thread-like deposits were found only in AD samples. Moreover, immunofluorescence analysis revealed that CD2AP colocalized with pTau. Regarding CD2AP neuronal distribution, a hierarchical progression from the entorhinal to the temporal and occipital cortex was detected. We found that CD2AP immunodetection in neurons was strongly and positively associated with Braak neurofibrillary stage, independent of age and other pathological hallmarks. To further investigate the association between pTau and CD2AP, we included samples from cases of primary tauopathies (corticobasal degeneration [CBD], progressive supranuclear palsy [PSP], and Pick's disease [PiD]) in our study. Among these cases, CD2AP positivity was only found in PiD samples as neurofibrillary tangle-like and Pick body-like deposits, whereas no neuronal CD2AP deposits were detected in PSP or CBD samples, which suggested an association of CD2AP neuronal expression with 3R-Tau-diseases. In conclusion, our findings open a new road to investigate the complex cellular mechanism underlying the tangle conformation and tau pathology in the brain.This work was funded by Instituto de Salud Carlos III (ISCIII) (PI17/00275, PI20/00465), cofinanced by the European Regional Development Fund (FEDER). The Neurovascular Research Laboratory is part of the INVICTUS+ network, ISCIII, Spain (RD16/0019/0021). M.H.-G. is supported by the Miguel Servet Programme, ISCIII, Spain (CPII17/00010

Targeted multiplex proteomics for molecular prescreening and biomarker discovery in metastatic colorectal cancer

Biomarcadores del cáncer; Cáncer metástico colorrectal; Terapias experimentalesCancer biomarkers; Colorectal metastatic cancer; Experimental therapiesBiomarcadors del càncer; Càncer metastàtic colorrectal; Teràpies experimentalsProtein biomarkers are widely used in cancer diagnosis, prognosis, and prediction of treatment response. Here we introduce the use of targeted multiplex proteomics (TMP) as a tool to simultaneously measure a panel of 54 proteins involved in oncogenic, tumour suppression, drug metabolism and resistance, in patients with metastatic colorectal cancer (mCRC). TMP provided valuable diagnostic information by unmasking an occult neuroendocrine differentiation and identifying a misclassified case based on abnormal proteins phenotype. No significant differences in protein levels between unpaired primary and metastatic samples were observed. Four proteins were found differentially expressed in KRAS-mutant as compared to wild-type tumours (overexpressed in mutant: KRAS, EGFR; overexpressed in wild-type: TOPO1, TOP2A). Survival analyses revealed the association between mesothelin expression and poor overall survival, whereas lack of PTEN protein expression associated with lower progression-free survival with anti-EGFR-based therapy in the first-line setting for patients with RAS wild-type tumour. Finally, outlier analysis identified putative targetable proteins in 65% of patients lacking a targetable genomic alteration. Our data show that TMP constitutes a promising, novel molecular prescreening tool in mCRC to identify protein expression alterations that may impact on patient outcomes and more precisely guide patient eligibility to clinical trials with novel targeted experimental therapies

Expression of TILs and Patterns of Gene Expression from Paired Samples of Malignant Pleural Mesothelioma (MPM) Patients

Gene expression; Immunotherapy; Malignant pleural mesotheliomaExpresión génica; Inmunoterapia; Mesotelioma pleural malignoExpressió gènica; Immunoteràpia; Mesotelioma pleural maligneMPM is an aggressive disease with an immunosuppressive tumor microenvironment, and interest in exploring immunotherapy in this disease has been increasing. In the first line of treatment, the combination of nivolumab and ipilimumab demonstrated an improvement in survival over chemotherapy. The presence of TILs has been recognized as a marker of antitumor immune response to chemotherapy in solid tumors. The aim of our study is to identify the effect of treatment on immune cells and the immune gene profile in MPM. We investigated the changes in expression of TILs in 10 human MPM paired tumor tissues using immunohistochemistry and gene expression analysis from paired untreated and treated samples. In this small series, we demonstrated that during the evolution of disease without any treatment there was an increase in the inflammatory component in tumor samples. After systemic treatment there was a decrease in the number of TILs. We observed that after systemic treatment or disease progression immune gene signatures were suppressed. Our integrated analysis of paired samples with immune profile and genomic changes on MPM suggested that during the evolution of the disease the immune system tends to switch, turning off with treatment.The study was partially funded by Project PREDICT-Meso (GEACC19003CED), funded by Fundación AECC

Immune cell profiling of the cerebrospinal fluid enables the characterization of the brain metastasis microenvironment

Brain metastases are the most common tumor of the brain with a dismal prognosis. A fraction of patients with brain metastasis benefit from treatment with immune checkpoint inhibitors (ICI) and the degree and phenotype of the immune cell infiltration has been used to predict response to ICI. However, the anatomical location of brain lesions limits access to tumor material to characterize the immune phenotype. Here, we characterize immune cells present in brain lesions and matched cerebrospinal fluid (CSF) using single-cell RNA sequencing combined with T cell receptor genotyping. Tumor immune infiltration and specifically CD8 + T cell infiltration can be discerned through the analysis of the CSF. Consistently, identical T cell receptor clonotypes are detected in brain lesions and CSF, confirming cell exchange between these compartments. The analysis of immune cells of the CSF can provide a non-invasive alternative to predict the response to ICI, as well as identify the T cell receptor clonotypes present in brain metastasis. The use of CSF for diagnosis of metastatic brain tumors could be of clinical and patient benefit. Here the authors undertake a single-cell RNA analysis of CSF and brain to determine whether the phenotype in the CSF is reflective of the phenotype in the tumo

Mosaizismo epigenetikoaren kuantifikazioa: protokoloaren diseinua

[EUS] Lan honen helburua da DMRen metilazioaren galera partziala duten PHP1B pazienteen alelo bakoitzeko CpG posizio bakoitzaren metilazio maila neurtzea ahalbidetzen duen metodo azkar, erraz eta merke bat diseinatzea, garatzea eta balioztatzea

Next Generation Immunohistochemistry (NGI): Unlocking the power of immunohistochemistry to improve biomarker analyses in precision oncology

Introducció. La medicina personalitzada promet el diagnòstic i el tractament de malalties a nivell individual i depèn en gran manera de la qualitat de les mostres clíniques i els assajos de diagnòstic. A mesura que es disposa d’opcions de tractament més específiques, es requereixen proves per a múltiples marcadors tumorals i es necessita optimitzar l’ús de mostres per a permetre un flux de treball de diagnòstic complet. A més, la caracterització de l’estat immunitari dels pacients es torna cada vegada més important per a la inmunooncología. L’extracció d’informació i els coneixements biològics requerits a partir de portaobjectes de teixit continua sent un desafiament amb els mètodes tradicionals basats ​​en teixits. Les dades de recerques recents han demostrat que els cicles iteratius de tinció i descoloració es poden realitzar en un sol portaobjectes de teixit sense perdre antigenicitat. No obstant això, la metodologia proposada era manual, laboriosa i lenta, la qual cosa limitava la seva aplicabilitat fora d’un entorn de recerca. Objectius. Planegem desenvolupar i validar panells de NGI específics compostos per diferents biomarcadors dissenyats per a abordar a) la quantificació del biomarcador KI67 en cèl·lules tumorals de manera reproduïble i automatitzada; b) la caracterització del microambient tumoral; i c) la caracterització dels fenotips de les cèl·lules tumorals de càncer de mama, heterogeneïtat i interaccions espacials amb cèl·lules T citotòxiques. Resultats. En el primer estudi es va dissenyar un panell NGI compost per KI67 per a la informació de proliferació i PanCK per a reconeixement de cèl·lules tumorals; el que ens va permetre, juntament amb l’anàlisi d’imatges, quantificar el KI67 en les cèl·lules tumorals de manera automàtica i solucionar els problemes de reproducibilitat que té la cuantificació del KI67. En el segon estudi es va dissenyar un panell compost per diferents biomarcadors per a explorar les cèl·lules T: CD3 (cèl·lules T), Foxp3 (cèl·lules T reguladores), CD4 (cèl·lules T auxiliars), CD8 (cèl·lules T citotòxiques), KI67 (marcador de proliferació), PanCK (reconeixement de células tumorals) que ens va permetre quantificar els diferents subtipus de cèl·lules T en la mostra, la seva distribució espacial i la seva anàlisi de proliferació. En el tercer estudi, un panell compost per KI67, receptor d’estrogen (ER), receptor de progesterona (PR), HER2 (receptor 2 del factor de creixement epidèrmic humà) i PanCK va proporcionar informació sobre l’expressió de cada biomarcador a nivell cel·lular; el que va permetre l’anàlisi de coexpresió en la mateixa cèl·lula, la seva distribució, la seva interacció espacial (cèl·lula tumoral-cèl·lula tumoral, cèl·lula tumoral-cèl·lules immunes) i els seus canvis durant el tractament anti-Her2. Conclusions. NGI ha estat desenvolupat, validat i utilitzat en diferents estudis. Pot ser utilitzat en qualsevol laboratori de patologia o recerca que estigui equipat per a patologia digital, d’una forma relativament senzilla i econòmica. Utilitza un únic portaobjectes per a cadascun dels panells, estalviant material per a anàlisis posteriors. NGI proporciona resultats reproduïbles que són comparables als IHC estàndard. NGI quantifica diferents biomarcadors a nivell d’una sola cèl·lula, proporcionant anàlisis de coexpresión i espacials; informació que ajuda a comprendre millor la biologia i la complexitat del tumor. Aquesta informació podria usar-se per a la predicció de la resposta a tractaments i per a identificar nous o millors biomarcadors per a predir la resposta al tractament i recolzar una millor estratificació dels pacients cap a diferents immunoteràpies o combinacions de teràpies.Introducción. La medicina personalizada promete el diagnóstico y el tratamiento de enfermedades a nivel individual y depende en gran medida de la calidad de las muestras clínicas y los ensayos de diagnóstico. A medida que se dispone de opciones de tratamiento más específicas, se requieren pruebas para múltiples marcadores tumorales y se necesita optimizar el uso de muestras para permitir un flujo de trabajo de diagnóstico completo. Además, la caracterización del estado inmunitario de los pacientes se vuelve cada vez más importante para la inmunooncología. La extracción de información y los conocimientos biológicos requeridos a partir de portaobjetos de tejido sigue siendo un desafío con los métodos tradicionales basados ​​en tejidos. Los datos de investigaciones recientes han demostrado que los ciclos iterativos de tinción y decoloración se pueden realizar en un solo portaobjetos de tejido sin perder antigenicidad. Sin embargo, la metodología propuesta era manual, laboriosa y lenta, lo que limitaba su aplicabilidad fuera de un entorno de investigación. Objetivos. Planeamos desarrollar y validar paneles de NGI específicos compuestos por diferentes biomarcadores diseñados para abordar a) la cuantificación del biomarcador KI67 en células tumorales de forma reproducible y automatizada; b) la caracterización del microambiente tumoral; y c) la caracterización de los fenotipos de las células tumorales de cáncer de mama, heterogeneidad e interacciones espaciales con células T citotóxicas. Resultados. En el primer estudio se diseñó un panel NGI compuesto por KI67 para la información de proliferación y PanCK para reconocimiento de células tumorales; lo que nos permitió, junto con el análisis de imágenes, cuantificar el KI67 en las células tumorales de forma automática y solucionar los problemas de reproducibilidad que tiene la cuantificación del KI67. En el segundo estudio se diseñó un panel compuesto por diferentes biomarcadores para explorar las células T: CD3 (células T), Foxp3 (células T reguladoras), CD4 (células T auxiliares), CD8 (células T citotóxicas), KI67 (marcador de proliferación), PanCK (reconocimiento de células tumorales) que nos permitió cuantificar los diferentes subtipos de células T en la muestra, su distribución espacial y su análisis de proliferación. En el tercer estudio, un panel compuesto por KI67, receptor de estrógeno (ER), receptor de progesterona (PR), HER2 (receptor 2 del factor de crecimiento epidérmico humano) y PanCK proporcionó información sobre la expresión de cada biomarcador a nivel celular; lo que permitió el análisis de coexpresión en la misma célula, su distribución, su interacción espacial (célula tumoral-célula tumoral, célula tumoral-células inmunes) y sus cambios durante el tratamiento anti-Her2. Conclusiones. NGI ha sido desarrollado, validado y utilizado en diferentes estudios. Puede ser utilizado en cualquier laboratorio de patología o investigación que esté equipado para patología digital, de una forma relativamente sencilla y económica. Utiliza un único portaobjetos para cada uno de los paneles, ahorrando material para análisis posteriores. NGI proporciona resultados reproducibles que son comparables a los IHC estándar. NGI cuantifica diferentes biomarcadores a nivel de una sola célula, proporcionando análisis de coexpresión y espaciales; información que ayuda a comprender mejor la biología y la complejidad del tumor. Esta información podría usarse para la predicción de la respuesta a tratamientos y para identificar nuevos o mejores biomarcadores para predecir la respuesta al tratamiento y respaldar una mejor estratificación de los pacientes hacia diferentes inmunoterapias o combinaciones de terapias.Introduction. Personalized medicine promises diagnosis and treatment of disease at the individual level and relies heavily on clinical specimen and diagnostic assay quality. As more targeted treatment options become available, testing for multiple tumor markers is required and optimization of sample use is needed to allow for a complete diagnostic workflow. In addition, characterization of the immune status of patients becomes increasingly important to immuno-oncology. The required extraction of information and biological insights from tissue slides remains challenging using traditional tissue-based methods. Recent research data have shown that iterative cycles of staining and destaining can be performed in a single tissue slide without losing antigenicity. However, the methodology proposed was manual, labor-intensive and time-consuming, thus limiting its applicability outside a research environment. Hypothesis. We have developed an innovative, simple, robust and automatized methodology, Next Generation Immunohistochemistry (NGI), to sequentially determine the expression of multiple individual biomarkers in a single tissue section. NGI may fill the actual limitations of the current approaches and become one of the multiplexed imaging technologies that could be used in different pathology and research laboratories to improve biomarker analyses in precision oncology. NGI may allow for a comprehensive characterization of biological tissue samples at cellular level while maintaining important spatial distribution/interaction between tumor and its microenvironment, information necessary to understand tumor biology and complexity. Objectives. We plan to develop and validate specific NGI panels composed of different biomarkers designed to address the a) quantification of KI67 biomarker in tumor cells in a reproducible and automated way; b) characterization of the tumor microenvironment; and c) characterization of breast cancer tumor cell phenotypes, heterogeneity and spatial interactions with cytotoxic t-cells. Results. In the first study, an NGI panel composed of KI67 for proliferation information and PanCK for tumor cell recognition was designed, which allowed us together with image analysis, to quantify KI67 in the tumor cells automatically and solve the reproducibility issues that KI67 index has. In the second study a panel composed of different biomarkers to explore the t-cells was designed: CD3 (t-cells), Foxp3 (regulatory t-cells), CD4 (helper t-cells), CD8 (cytotoxic t-cells), KI67 (proliferation marker), PanCK (tumor recognition) that allowed us the quantification of the different t-cell subtypes in the sample, their spatial distribution and their proliferation analyses. In the third study a panel composed of KI67, estrogen receptor (ER), progesteron receptor (PR), HER2 (human epidermal growth factor receptor-2) and PanCK provided information on the expression of each biomarker at a single-cell level, allowing analysis of their co-expression in the same cell, their distribution, their spatial interaction (tumor cell-tumor cell, tumor cell-immune cells) and their changes during anti-Her2 treatment. Conclusions. NGI has been developed, validated and used in different studies. It can be used in any pathology or research laboratory that is equipped for digital pathology, in a relatively simple and inexpensive way. It only uses a single slide for each of the panels saving material for further analyses. NGI provides reproducible results that are comparable to standard IHCs. NGI quantifies different biomarkers at a single cell level, providing co-expression and spatial analyses, information that helps deeper understand the tumor biology and complexity; information that could be used for response prediction and for identifying better or new biomarkers to predict response to treatment and supporting better patient stratification towards different immunotherapies or therapy combinations.Universitat Autònoma de Barcelona. Programa de Doctorat en Cirurgia i Ciències Morfològique

Next Generation Immunohistochemistry (NGI) : Unlocking the power of immunohistochemistry to improve biomarker analyses in precision oncology

Introducció. La medicina personalitzada promet el diagnòstic i el tractament de malalties a nivell individual i depèn en gran manera de la qualitat de les mostres clíniques i els assajos de diagnòstic. A mesura que es disposa d'opcions de tractament més específiques, es requereixen proves per a múltiples marcadors tumorals i es necessita optimitzar l'ús de mostres per a permetre un flux de treball de diagnòstic complet. A més, la caracterització de l'estat immunitari dels pacients es torna cada vegada més important per a la inmunooncología. L'extracció d'informació i els coneixements biològics requerits a partir de portaobjectes de teixit continua sent un desafiament amb els mètodes tradicionals basats ​​en teixits. Les dades de recerques recents han demostrat que els cicles iteratius de tinció i descoloració es poden realitzar en un sol portaobjectes de teixit sense perdre antigenicitat. No obstant això, la metodologia proposada era manual, laboriosa i lenta, la qual cosa limitava la seva aplicabilitat fora d'un entorn de recerca. Objectius. Planegem desenvolupar i validar panells de NGI específics compostos per diferents biomarcadors dissenyats per a abordar a) la quantificació del biomarcador KI67 en cèl·lules tumorals de manera reproduïble i automatitzada; b) la caracterització del microambient tumoral; i c) la caracterització dels fenotips de les cèl·lules tumorals de càncer de mama, heterogeneïtat i interaccions espacials amb cèl·lules T citotòxiques. Resultats. En el primer estudi es va dissenyar un panell NGI compost per KI67 per a la informació de proliferació i PanCK per a reconeixement de cèl·lules tumorals; el que ens va permetre, juntament amb l'anàlisi d'imatges, quantificar el KI67 en les cèl·lules tumorals de manera automàtica i solucionar els problemes de reproducibilitat que té la cuantificació del KI67. En el segon estudi es va dissenyar un panell compost per diferents biomarcadors per a explorar les cèl·lules T: CD3 (cèl·lules T), Foxp3 (cèl·lules T reguladores), CD4 (cèl·lules T auxiliars), CD8 (cèl·lules T citotòxiques), KI67 (marcador de proliferació), PanCK (reconeixement de células tumorals) que ens va permetre quantificar els diferents subtipus de cèl·lules T en la mostra, la seva distribució espacial i la seva anàlisi de proliferació. En el tercer estudi, un panell compost per KI67, receptor d'estrogen (ER), receptor de progesterona (PR), HER2 (receptor 2 del factor de creixement epidèrmic humà) i PanCK va proporcionar informació sobre l'expressió de cada biomarcador a nivell cel·lular; el que va permetre l'anàlisi de coexpresió en la mateixa cèl·lula, la seva distribució, la seva interacció espacial (cèl·lula tumoral-cèl·lula tumoral, cèl·lula tumoral-cèl·lules immunes) i els seus canvis durant el tractament anti-Her2. Conclusions. NGI ha estat desenvolupat, validat i utilitzat en diferents estudis. Pot ser utilitzat en qualsevol laboratori de patologia o recerca que estigui equipat per a patologia digital, d'una forma relativament senzilla i econòmica. Utilitza un únic portaobjectes per a cadascun dels panells, estalviant material per a anàlisis posteriors. NGI proporciona resultats reproduïbles que són comparables als IHC estàndard. NGI quantifica diferents biomarcadors a nivell d'una sola cèl·lula, proporcionant anàlisis de coexpresión i espacials; informació que ajuda a comprendre millor la biologia i la complexitat del tumor. Aquesta informació podria usar-se per a la predicció de la resposta a tractaments i per a identificar nous o millors biomarcadors per a predir la resposta al tractament i recolzar una millor estratificació dels pacients cap a diferents immunoteràpies o combinacions de teràpies.Introducción. La medicina personalizada promete el diagnóstico y el tratamiento de enfermedades a nivel individual y depende en gran medida de la calidad de las muestras clínicas y los ensayos de diagnóstico. A medida que se dispone de opciones de tratamiento más específicas, se requieren pruebas para múltiples marcadores tumorales y se necesita optimizar el uso de muestras para permitir un flujo de trabajo de diagnóstico completo. Además, la caracterización del estado inmunitario de los pacientes se vuelve cada vez más importante para la inmunooncología. La extracción de información y los conocimientos biológicos requeridos a partir de portaobjetos de tejido sigue siendo un desafío con los métodos tradicionales basados ​​en tejidos. Los datos de investigaciones recientes han demostrado que los ciclos iterativos de tinción y decoloración se pueden realizar en un solo portaobjetos de tejido sin perder antigenicidad. Sin embargo, la metodología propuesta era manual, laboriosa y lenta, lo que limitaba su aplicabilidad fuera de un entorno de investigación. Objetivos. Planeamos desarrollar y validar paneles de NGI específicos compuestos por diferentes biomarcadores diseñados para abordar a) la cuantificación del biomarcador KI67 en células tumorales de forma reproducible y automatizada; b) la caracterización del microambiente tumoral; y c) la caracterización de los fenotipos de las células tumorales de cáncer de mama, heterogeneidad e interacciones espaciales con células T citotóxicas. Resultados. En el primer estudio se diseñó un panel NGI compuesto por KI67 para la información de proliferación y PanCK para reconocimiento de células tumorales; lo que nos permitió, junto con el análisis de imágenes, cuantificar el KI67 en las células tumorales de forma automática y solucionar los problemas de reproducibilidad que tiene la cuantificación del KI67. En el segundo estudio se diseñó un panel compuesto por diferentes biomarcadores para explorar las células T: CD3 (células T), Foxp3 (células T reguladoras), CD4 (células T auxiliares), CD8 (células T citotóxicas), KI67 (marcador de proliferación), PanCK (reconocimiento de células tumorales) que nos permitió cuantificar los diferentes subtipos de células T en la muestra, su distribución espacial y su análisis de proliferación. En el tercer estudio, un panel compuesto por KI67, receptor de estrógeno (ER), receptor de progesterona (PR), HER2 (receptor 2 del factor de crecimiento epidérmico humano) y PanCK proporcionó información sobre la expresión de cada biomarcador a nivel celular; lo que permitió el análisis de coexpresión en la misma célula, su distribución, su interacción espacial (célula tumoral-célula tumoral, célula tumoral-células inmunes) y sus cambios durante el tratamiento anti-Her2. Conclusiones. NGI ha sido desarrollado, validado y utilizado en diferentes estudios. Puede ser utilizado en cualquier laboratorio de patología o investigación que esté equipado para patología digital, de una forma relativamente sencilla y económica. Utiliza un único portaobjetos para cada uno de los paneles, ahorrando material para análisis posteriores. NGI proporciona resultados reproducibles que son comparables a los IHC estándar. NGI cuantifica diferentes biomarcadores a nivel de una sola célula, proporcionando análisis de coexpresión y espaciales; información que ayuda a comprender mejor la biología y la complejidad del tumor. Esta información podría usarse para la predicción de la respuesta a tratamientos y para identificar nuevos o mejores biomarcadores para predecir la respuesta al tratamiento y respaldar una mejor estratificación de los pacientes hacia diferentes inmunoterapias o combinaciones de terapias.Introduction. Personalized medicine promises diagnosis and treatment of disease at the individual level and relies heavily on clinical specimen and diagnostic assay quality. As more targeted treatment options become available, testing for multiple tumor markers is required and optimization of sample use is needed to allow for a complete diagnostic workflow. In addition, characterization of the immune status of patients becomes increasingly important to immuno-oncology. The required extraction of information and biological insights from tissue slides remains challenging using traditional tissue-based methods. Recent research data have shown that iterative cycles of staining and destaining can be performed in a single tissue slide without losing antigenicity. However, the methodology proposed was manual, labor-intensive and time-consuming, thus limiting its applicability outside a research environment. Hypothesis. We have developed an innovative, simple, robust and automatized methodology, Next Generation Immunohistochemistry (NGI), to sequentially determine the expression of multiple individual biomarkers in a single tissue section. NGI may fill the actual limitations of the current approaches and become one of the multiplexed imaging technologies that could be used in different pathology and research laboratories to improve biomarker analyses in precision oncology. NGI may allow for a comprehensive characterization of biological tissue samples at cellular level while maintaining important spatial distribution/interaction between tumor and its microenvironment, information necessary to understand tumor biology and complexity. Objectives. We plan to develop and validate specific NGI panels composed of different biomarkers designed to address the a) quantification of KI67 biomarker in tumor cells in a reproducible and automated way; b) characterization of the tumor microenvironment; and c) characterization of breast cancer tumor cell phenotypes, heterogeneity and spatial interactions with cytotoxic t-cells. Results. In the first study, an NGI panel composed of KI67 for proliferation information and PanCK for tumor cell recognition was designed, which allowed us together with image analysis, to quantify KI67 in the tumor cells automatically and solve the reproducibility issues that KI67 index has. In the second study a panel composed of different biomarkers to explore the t-cells was designed: CD3 (t-cells), Foxp3 (regulatory t-cells), CD4 (helper t-cells), CD8 (cytotoxic t-cells), KI67 (proliferation marker), PanCK (tumor recognition) that allowed us the quantification of the different t-cell subtypes in the sample, their spatial distribution and their proliferation analyses. In the third study a panel composed of KI67, estrogen receptor (ER), progesteron receptor (PR), HER2 (human epidermal growth factor receptor-2) and PanCK provided information on the expression of each biomarker at a single-cell level, allowing analysis of their co-expression in the same cell, their distribution, their spatial interaction (tumor cell-tumor cell, tumor cell-immune cells) and their changes during anti-Her2 treatment. Conclusions. NGI has been developed, validated and used in different studies. It can be used in any pathology or research laboratory that is equipped for digital pathology, in a relatively simple and inexpensive way. It only uses a single slide for each of the panels saving material for further analyses. NGI provides reproducible results that are comparable to standard IHCs. NGI quantifies different biomarkers at a single cell level, providing co-expression and spatial analyses, information that helps deeper understand the tumor biology and complexity; information that could be used for response prediction and for identifying better or new biomarkers to predict response to treatment and supporting better patient stratification towards different immunotherapies or therapy combinations