Intratumoral Heterogeneity Research Articles

The RECONCILE study protocol: Exploiting image-based risk stratification in early prostate cancer to discriminate progressors from non-progressors (RECONCILE).

RECONCILE (ClinicalTrials.gov:NCT04340245) will identify molecular and radiomic markers associated with clinical progression and radiological progression events in a cohort of localised, newly diagnosed Gleason 3 + 4 tumours. Molecular markers will be correlated against standard of care MRI-targeted histology and oncological outcomes. RECONCILE is an ethics approved (20/LO/0366) single centre, prospective, longitudinal, observational cohort study of recently diagnosed (within 12 months), organ-confined Gleason 3 + 4 cancers (MCCL ≤10mm) currently under active surveillance. 60 treatment-naïve participants with a concordant MRI lesion (Likert score 4 or 5) and PSA ≤ 15 ng/ml will be recruited. Blood, urine and targeted prostate tissue cores will be subject to next generation sequencing at baseline and one year in all participants. Semen will be collected from a specified sub-population. Baseline and interval MR images will be extracted from standard of care prostate MRI ahead of radiomic analysis. Data extracted from radiological and biological samples will be used to derive the association of molecular change and radiological progression, the primary outcome of the study. To compensate for spatial intratumoral heterogeneity and inherent sampling bias, a molecular index will be derived for each participant using the molecular profile of tumour tissue at both baseline (MolBL) and one year (MolFU). We will extract a ΔMolBL:MolFU score for each participant. Molecular progression will be defined as a MolBL:MolFU score >95% CI of the combined ΔMolBL scores. Radiological progression is defined as a PRECISE score of 4 or 5. The study is powered to detect an association with a statistical power of 80%. Recruitment began in July 2020 (n = 62). To date, 37 participants have donated tissue for analysis. We have designed and implemented a prospective, longitudinal study to evaluate the underlying molecular landscape of intermediate risk, MR-visible prostate tumours. Recruitment is ongoing.

Two distinct epithelial-to-mesenchymal transition programs control invasion and inflammation in segregated tumor cell populations.

Epithelial-to-mesenchymal transition (EMT) triggers cell plasticity in embryonic development, adult injured tissues and cancer. Combining the analysis of EMT in cell lines, embryonic neural crest and mouse models of renal fibrosis and breast cancer, we find that there is not a cancer-specific EMT program. Instead, cancer cells dedifferentiate and bifurcate into two distinct and segregated cellular trajectories after activating either embryonic-like or adult-like EMTs to drive dissemination or inflammation, respectively. We show that SNAIL1 acts as a pioneer factor in both EMT trajectories, and PRRX1 drives the progression of the embryonic-like invasive trajectory. We also find that the two trajectories are plastic and interdependent, as the abrogation of the EMT invasive trajectory by deleting Prrx1 not only prevents metastasis but also enhances inflammation, increasing the recruitment of antitumor macrophages. Our data unveil an additional role for EMT in orchestrating intratumor heterogeneity, driving the distribution of functions associated with either inflammation or metastatic dissemination.

Morphologic Characterization of Pancreatic Ductal Adenocarcinoma following Post-neoadjuvant Pancreatectomy and Clinical value of Intratumor Heterogeneity.

To evaluate the morphologic landscape of pancreatic ductal adenocarcinoma (PDAC), intratumor spatial heterogeneity, and the resulting clinical impact following post-neoadjuvant pancreatectomy. The clinical value of PDAC morphologic subtypes and intratumor spatial heterogeneity post-treatment remains an open issue. The study cohort included patients who underwent post-neoadjuvant pancreatectomy for PDAC at the University of Verona Hospital Trust between 2013 and 2019. All hematoxylin and eosin-stained slides were reviewed to assess PDAC histomorphology and intratumor heterogeneity. The relationship with other clinicopathological variables, overall survival (OS), and recurrence-free (RFS) survival was evaluated using standard statistics. The study cohort included 400 patients. Histological revision identified ten different morphologic subtypes. Gland-forming PDAC with a conventional pattern was the most frequently identified subtype (41.8%). Overall, 247 tumors (61.7%) showed only one histological pattern and were classified as homogeneous, whereas 153 (38.3%) showed different morphologies and were classified as heterogeneous tumors. The median post-resection survival was 30.1 months (95%CI 26.6-33.5). There was a substantial survival variability according to the morphologic subtype, ranging from 19.1 months in the gyriform subtype to 47.0 months in the papillary subtype. Tumors with a heterogeneous morphology displayed a higher rate of nodal metastases, worse tumor regression metrics, and worse oncologic outcomes relative to spatially homogeneous tumors. This paper provided a morphological taxonomy of residual tumors following post-neoadjuvant pancreatectomy for PDAC. The morphologic subtype and intratumor spatial heterogeneity have relevant prognostic implications and could be included in the pathology report to complement regression metrics.

MENINGIOMA SIZE IS POSITIVELY CORRELATED WITH TUMOR WHO GRADE

Abstract AIMS It is well established that the surgical outcome is poorer in larger meningiomas. Vascular/ neural encasement and bone/dural/vascular invasion complicate surgical resection. Nevertheless, it is also likely that meningiomas will become biologically more aggressive with increasing size and tumor cell burden. To prove this hypothesis we studied radiological features of 316 meningiomas and correlated these features with tumor WHO-grade. METHOD 316 meningioma patients who were operated on and who received pathological diagnoses at one single institution were analyzed. Pathological grading was revised by one single neuropathologist according to WHO 2021 criteria. 123 high-grade (WHO grade 2 and 3) patients were initially selected and later substituted with a further 193 age and anatomical-location-matched individuals. Preoperative MRI studies were analyzed for size, hyperostosis, intratumoral calcification, mass effect, peritumoral edema, shape (globular/en-plaque), lobulation, cystic component, intratumoral heterogeneity, dural tail, and necrosis/hemorrhage. Univariate analysis as well as logistic regression were performed. RESULTS High-grade meningiomas were significantly more common to have lobulation (p: 7.111 x 10-8), necrosis/hemorrhage (p: 1.176 x 10-6), peritumoral edema (p: 3.421 x 10-6), intratumoral heterogeneity (p: 1.201 x 10-5), mass effect (p: 0.0002408), and cystic component (p: 0.003519) compared to WHO grade 1 meningiomas. Logistic regression indicated that tumor volume was an independent and significant parameter. High-grade tumors were significantly larger (40.83 cm3 vs 22.18 cm3, p: 3.518 x 10-6). Skull base meningiomas were of significantly lower grade (p: 0.001318). Mean volume was significantly higher in high-grade meningiomas. Mean tumor volume varied significantly among histopathological subtypes but was highest in anaplastic and atypical subtypes. Tumor volume was strongly correlated with mitosis (p: 2.353 x 10-6), hypercellularity (p: 1.22 x 10-4), spontaneous necrosis (p: 3.273 x 10-4), brain invasion (p: 0.02491), macronucleus (p: 0.05327), small cell (p: 0.09425), and ki-67 levels (p: 2x10-16). CONCLUSION Tumor size is strongly correlated with WHO grade in meningiomas.

Reconstruction of cancer marker analysis with holistic anatomical precision implicates heterogeneity development during breast tumor progression

Biomarkers are not only of significant importance for cancer diagnosis and selection of treatment plans but also recently increasingly used for the evaluation of malignancy development and tumor heterogeneity. Large-size tumors from clinical patients can be unique and valuable sources for the study of cancer progression, particularly to the extent of intratumoral heterogeneity. In the present study, we obtained a series of post-surgery puncture samples from a breast cancer patient with a 4 × 3.5 × 2 cm tumor in its original size. Immunohistochemistry for Ki-67, COX-2, and CA IX was performed and the expression levels within the breast cancer tumor mass were evaluated in the reconstructed 3D models. To further evaluate the intratumoral heterogeneity, we performed high throughput whole transcriptome sequencing of 12 samples from different spatial positions within the tumor tissue. Comparing the reconstructed 3D distribution of biomarkers with projected tumor growth models, asymmetric and heterogeneous expansion of tumor mass was found to be possibly influenced by factors such as blood supply, inflammation and/or hypoxia stimulations, as suggested from the correlation between the results of Ki-67 and CA IX or COX-2 staining. Furthermore, high-throughput RNA sequencing data provided additional information for profiling the intratumoral heterogeneity and expanded the understanding of cancer progression. Digital technology for medical imaging once properly integrated with molecular pathology examinations will become particularly helpful in dissecting out in-depth information for precision medicine. We prospect that this approach, facilitated by rapidly advancing artificial intelligence, could provide new insights for clinical decision-making in the future. Strategies for the continuous development from the present study for better performance and application were discussed.

MULTI-OMIC INTEGRATION REVEAL INTRA-TUMOUR HETEROGENEITY AND IDENTIFY DISULfiRAM AND COPPER AS A SYNERGISTIC THERAPY IN PAEDIATRIC EPENDYMOMA

Abstract AIMS Ependymoma (EPN) is the second most malignant paediatric brain tumour. The PF-A subgroup, associated with a hypoxic microenvironment, has a dismal survival rate of 50% with clinical trials failing to demonstrate a significant survival advantage from chemotherapy to date. As the lack of genomic alterations makes it difficult to uncover novel therapeutic targets, we present a multi-omic integration investigating whether spatially-distinct tumour microenvironments represent targetable metabolic niches in PF-A EPN. METHOD Surgical sampling of spatially-distinct regions was performed on eight PF-A patients. Metabolites and RNA were simultaneously extracted from the same cellular population and analysed using liquid chromatography-mass spectrometry (LC-MS) and RNAseq. Multi-omic integration was performed using Metscape3 and functional assays evaluating proliferation, invasion and siRNA-mediated knockdown were performed in 2D and 3D models of intra-tumour region patient-derived cell lines. Selected single and drug combination therapy was performed in normoxia and hypoxia, and synergy assessed using the Chou-Talalay method. RESULTS Multi-omic integration revealed 124 dysregulated metabolic pathways, encompassing 156 genes and 49 metabolites with the largest number of interactions occurring in the gluconeogenesis pathway. Each anatomical region also presented at least one unique gene-metabolite interaction demonstrating heterogeneity within and across PF-A EPN tumours. Four selected drug targets (Disulfiram, Vandetanib, Mildronate and FBP1 inhibitor) based on metabolically relevant genes (ALDH3A1, FMO3, BBOX1, FBP1) showed impairment of metabolic viability in vitro, with limited chemosensitivity observed in control human cerebellar astrocytes. The addition of Cu2+ significantly sensitized cells to Disulfiram, particularly under hypoxic conditions. This synergistic combination significantly impaired invasion in both normoxia and hypoxia. CONCLUSION This is the first instance where multi-omic data integration and intra-tumour heterogeneity has been investigated for paediatric EPN revealing novel therapeutic targets in the context of gene-metabolite correlations. Drug tolerability will be further assessed in vivo using the previously characterised PF-A EPN orthotopic mouse xenograft MAF-928.

Discordant Microsatellite Instability Status in Metachronous Primary Rectal Cancer Detected During IdyllaTM Microsatellite Instability Test Validation: A Case Report

Abstract Introduction/Objective Defects in DNA mismatch repair (MMR) can be identified by immunohistochemistry (IHC) MMR, Microsatellite Instability (MSI) status testing by PCR or by MSI testing by NGS. Discrepancies may occur between the different test methods. We describe a case of a discrepancy discovered during validation of an in-house IdyllaTM MSI test (PCR method), in which repeat testing by the external reference lab led to a definitive deficient MMR/MSI-High (MSI-H) status. Methods/Case Report A 67-year-old male presented with a new rectal mass on a follow-up colonoscopy. His past surgical history included a colorectal anastomosis secondary to a low anterior resection for pT3N0 upper rectal cancer ten years ago. He had a strong family history of colorectal cancer with his father, 3 brothers, 3 paternal uncles and 2 cousins affected. Colonoscopy revealed an adenocarcinoma 3 cm distal to the anastomosis. MRI showed a plaque-like mucosal thickening along the remaining rectum below the anastomosis. He underwent a complicated redo low anterior resection. Histopathology revealed a pT1N0 invasive moderately differentiated midrectal adenocarcinoma. Test results for an external PCR-based MSI test were microsatellite stable (MSS). However, repeat testing (requested after MSI-H status was detected by the in-house IdyllaTM MSI assay) revealed a deficient MMR status (loss of expression of MSH2 and MSH6 proteins) and MSI-H by PCR analysis. These discrepant results may have been due to low tumor cellularity and tumor heterogeneity (ie, heterogeneous pattern of MSH2/MSH6 loss on IHC). Results (if a Case Study enter NA) NA Conclusion Discrepancies in deficient MMR/MSI status between and within different test methods may be attributable to intratumoral heterogeneity and low tumor cellularity. In discordant cases, pathologists must not hesitate to perform a repeat analysis using alternate methods, as a deficient MMR/MSI-H diagnosis has crucial diagnostic, prognostic and predictive implications.

Spatial multiomics reveal intratumoral immune heterogeneity with distinct cytokine networks in lung cancer brain metastases.

The tumor microenvironment of brain metastases has become a focus in the development of immunotherapeutic drugs. However, countless brain metastasis patients have not experienced clinical benefit. Thus, understanding the immune cell composition within brain metastases, and how the immune cells interact with each other and other microenvironmental cell types, may be critical for optimizing immunotherapy. We applied spatial whole transcriptomic profiling with extensive multiregional sampling (19-30 regions per sample) and multiplex immunohistochemistry on formalin-fixed, paraffin-embedded lung cancer brain metastasis samples. We performed deconvolution of gene expression data to infer the abundances of immune cell populations and inferred spatial relationships from the multiplex immunohistochemistry data. We also described cytokine networks between immune and tumor cells and used a protein language model to predict drug-target interactions. Finally, we performed deconvolution of bulk RNA data to assess the prognostic significance of immune-metastatic tumor cellular networks. We show that immune cell infiltration has a negative prognostic role in lung cancer brain metastases. Our in-depth multiomics analyses further reveal recurring intratumoral immune heterogeneity and the segregation of myeloid and lymphoid cells into distinct compartments that may be influenced by distinct cytokine networks. By employing computational modeling, we identify drugs that may target genes expressed in both tumor core and regions bordering immune infiltrates. Finally, we illustrate the potential negative prognostic role of our immune-metastatic tumor cellular networks. Our findings advocate for a paradigm shift from focusing on individual genes or cell types, towards targeting networks of immune and tumor cells.

Targeting DLBCL by mutation-specific disruption of cancer-driving oncogenes.

Diffuse large B cell lymphomas (DLBCL) are highly aggressive tumors. Their genetic complexity and heterogeneity have hampered the development of novel approaches for precision medicine. Our study aimed to develop a personalized therapy for DLBCL by utilizing the CRISPR/Cas system to induce knockouts (KO) of driver genes, thereby causing cancer cell death while minimizing side effects. We focused on OCI-LY3 cells, modeling DLBCL, and compared them with BJAB cells as controls. Analysis of whole exome sequencing revealed significant mutations in genes like PAX5, CD79B, and MYC in OCI-LY3 cells. CRISPR/Cas9-mediated KO of these genes resulted in reduced cancer cell viability. Subsequent single and dual gRNA targeting of PAX5 mutations inhibited proliferation specifically in OCI-LY3 cells. Moreover, dual gRNA targeting of PAX5 and MYC induced chromosomal rearrangements, reducing cell proliferation substantially. However, targeting single intronic mutations did not affect cell viability, highlighting the importance of disrupting protein function. Targeting multiple mutations simultaneously addresses intra-tumoral heterogeneity, and the transient delivery of CRISPR/Cas9 allows for permanent gene disruption. While challenges such as incomplete editing efficiency and delivery limitations exist, further optimization may enhance therapeutic efficacy. Overall, our findings demonstrate the efficacy of CRISPR/Cas9 in targeting oncogenic mutations, opening avenues for precision medicine in DLBCL treatment.

Flower-Shaped Plasma Cells in Multiple Myeloma with Morphological Heterogeneity.

Flower-shaped nuclei in plasma cells are rare in multiple myeloma. We report on an 88-year-old male who presented with a mass lesion in the clavicular region. A biopsy of the mass revealed an increase in mature plasma cells with round nuclei. In contrast, a bone marrow examination showed increased plasma cells with flower-shaped nuclei. The patient tested negative for human T-lymphotropic virus type-1 and was diagnosed with multiple myeloma. While multiple myeloma is known for intra-tumor heterogeneity, reports of morphological heterogeneity based on the site of tumor sampling are limited. In this case, the presence of plasma cells with flower-shaped nuclei enabled the identification of site-dependent morphological tumor heterogeneity.

Computed tomography perfusion as a predictor of gastric cancer grades

BackgroundGastric cancer stands as one of the most prevalent malignancies globally, conventional endoscopic specimens have been the primary means of diagnosing preoperative gastric histopathology, however, their limitations in capturing intra-tumor heterogeneity compromise their efficacy in evaluating angiogenesis. Perfusion Computed Tomography (P-CT) emerges as a pivotal functional imaging modality, facilitating objective assessment of tissue perfusion, serving as a marker of angiogenesis. So, our research objective was to evaluate the efficacy of CT perfusion imaging in the prediction of histological grades of gastric tumors using quantitative perfusion parameters such as permeability surface (PS), blood flow (BF), mean transient time (MTT), and blood volume (BV), in addition to the qualitative scoring system then comparing the findings with the histopathological results.ResultsPS and BF were statistically significant predictors of the grade of differentiation, their odds ratio (OR) was (1.05, 95% CI 1.02–1.09, for each of them) (P = 0.004, P = 0.009, respectively). MTT also emerged as a significant predictor of the grade of differentiation with an OR of 0.76 (95% CI 0.57–0.93, P = 0.025). Using multivariate logistic regression model, PS was the most potent individual P-CT predictor of differentiation of the grade and the diagnosis of poorly differentiated tumors at ≥ 39 mL/100 g/min cut off point, followed by BF at ≥ 82.2 mL/100 g/min, and MTT at < 8.4 s. Regarding the qualitative scoring system P-CT, poorly differentiated tumors generally received higher scores of PS (P < 0.001), BF (P < 0.001), and BV (P = 0.017), than well and moderately differentiated tumors, however, MTT showed that poorly differentiated tumors were more frequently scored as low compared to well and moderately differentiated tumors (P < 0.001).ConclusionsP-CT is an innovative, non-invasive biomarker for predicting gastric cancer grade by quantitative and qualitative assessment by P-CT parameters (PS, BF and MTT) with particular role of PS as the strongest individual P-CT predictor of differentiation grade followed by BF and MTT at specific cut off points.

Classification of histological subtypes of non-small cell lung cancer using computerized tomography texture analysis

Objective: This study aimed to differentiate between the two main histological subtypes of non-small cell lung cancer using a non-invasive technique, computerized tomography texture analysis. Method: We included 53 patients. All patients were histopathologically proven non-small cell lung cancer cases. All patients underwent thorax CT scans. In CT images, the differences present in the texture features of adenocarcinoma and squamous cell carcinoma, which are the two main histological subtypes of non-small cell lung cancer, were determined by the consensus of two radiologists for computerized tomography-based texture analysis. Results: A total of 44 texture features were extracted, including 12 first-order features and 32 second-order features derived from gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone length matrix (GLZLM) features in 51 CT images. None of the evaluated texture parameters were statistically significant. However, in patients with squamous cell lung cancer, the values of Intensity Histogram, NGTDM Complexity, and Intensity Based Robust Mean Absolute Deviation higher from adenocarcinoma patients and had the highest area under the curve in roc analyses (0.727, 0.664, 0.666 respectively) Conclusion: Intensity Histogram, NGTDM Complexity, and Intensity Based Robust Mean Absolute Deviation features can be used to differentiate between the subtypes of non-small cell lung cancer, adenocarcinoma and squamous cell carcinoma. These features were highly associated with the high intratumoral heterogeneity of squamous cell lung cancer.

Intratumoral Heterogeneity and Metabolic Cross-Feeding in a Three-Dimensional Breast Cancer Culture: An In Silico Perspective.

Today, the intratumoral composition is a relevant factor associated with the progression and aggression of cancer. Although it suggests a metabolic interdependence among the subpopulations inside the tumor, a detailed map of how this interdependence contributes to the malignant phenotype is still lacking. To address this issue, we developed a systems biology approach integrating single-cell RNASeq and genome-scale metabolic reconstruction to map the metabolic cross-feeding among the subpopulations previously identified in the spheroids of MCF7 breast cancer. By calibrating our model with expression profiles and the experimental growth rate, we concluded that the reverse Warburg effect emerges as a mechanism to optimize community growth. Furthermore, through an in silico analysis, we identified lactate, alpha-ketoglutarate, and some amino acids as key metabolites whose disponibility alters the growth rate of the spheroid. Altogether, this work provides a strategy for assessing how space and intratumoral heterogeneity influence the metabolic robustness of cancer, issues suggesting that computational strategies should move toward the design of optimized treatments.

Whole tumour- and subregion-based radiomics of contrast-enhanced mammography in differentiating HER2 expression status of invasive breast cancers: A double-centre pilot study.

To explore the value of whole tumour- and subregion-based radiomics of contrast-enhanced mammography (CEM) in differentiating the HER2 expression status of breast cancers. 352 patients underwent preoperative CEM from two centres were consecutively enroled and divided into the training, internal validation, and external validation cohorts. The lesions were divided into HER2-positive and HER2-negative groups. Besides the radiological features, radiomics features capturing the whole tumour-based (wITH) and subregion-based intratumoral heterogeneity (sITH) were extracted from the craniocaudal view of CEM recombined images. The XGBoost classifier was applied to develop the radiological, sITH, and wITH models. A combined model was constructed by fusing the prediction results of the three models. The mean age of the patients was 51.1 ± 10.7 years. Two radiological features, four wITH features, and three sITH features were selected to establish the models. The combined model significantly improved the AUC to 0.80 ± 0.03 (95% CI: 0.73-0.86), 0.79 ± 0.06 (95% CI: 0.67-0.90), and 0.79 ± 0.05 (95% CI: 0.69-0.89) in the training, internal validation, and external validation cohorts, respectively (All P < 0.05). The combined model showed good agreement between the predicted and observed probabilities and favourable net clinical benefit in the validation cohorts. Both whole tumour- and subregion-based ITH radiomics features of CEM exhibited potential for differentiating the HER2 expression status. Combining conventional radiological features and ITH features can improve the model's performance.

A basement membrane-related signature for prognosis and immunotherapy benefit in bladder cancer based on machine learning

BackgroundBladder cancer has a poor clinical outcome because of its high aggressiveness. Basement membrane plays vital functions in tumor invasion and migration. Invasion and distant metastasis of cancer are facilitated by degradation of the basement membrane and extracellular matrix.MethodsTen machine learning methods were utilized to develop the basement membrane-related signature (MRS) using datasets from TCGA, GSE13507, GSE31684, GSE32984 and GSE48276. Three anti-PD1 or anti-CTLA4 datasets and several predicting scores were used to investigate the performance of MRS in predicting the immunotherapy benefits.ResultsA predicting model based on the Enet algorithm (alpha = 0.1) was chosen as the optimal MRS since it had the highest average C-index being 0.72. According to TCGA data, the MRS showed good performance in predicting bladder cancer patients' clinical outcomes, with area under curves of 0.744, 0.766 and 0.817 for 1, 3, and 5-year receiver operating characteristic curve, respectively. PD1 and CTLA4 immunophenoscopes were associated with a low MRS score, as well as a lower tumor immune dysfunction and exclusion score. As MRS score increased, immune-activated cells levels decreased, tumor immune dysfunction and exclusion score decreased, immune escape score decreased, intratumor heterogeneity score decreased, PD1&CTLA4 immunophenoscore increased, and tumor mutational burden score increased in bladder cancer, suggesting better immunotherapy benefits. Bladder cancer cases with high MRS score was correlated with higher cancer related hallmark scores, including NOTCH and glycolysis signaling.ConclusionA new MRS has been developed for bladder cancer, which could be used to predict prognosis and the success of immunotherapy.

Single-cell multiplex immunocytochemistry in cell block preparations of metastatic breast cancer confirms sensitivity of GATA-binding protein 3 over gross cystic disease fluid protein 15 and mammaglobin.

Metastatic breast cancers are frequently encountered in cytology and require immunocytochemistry (ICC). In this study, traditional and multiplex ICC (mICC) for GATA-binding protein 3 (GATA3), gross cystic disease fluid protein 15 (GCDFP15), and mammaglobin (MMG) were performed with the aim of validating mICC in cell blocks, with further single-cell expression pattern analysis to identify the single markers and combinations of markers most sensitive in subtypes of breast cancer. GATA3, GCDFP15, and MMG were paired with OptiView 3,3'-diaminobenzidine and Ventana DISCOVERY Purple and Blue, respectively, with cyclical and serial staining. Bright-field imaging was performed with the Mantra 2 system and analyzed with the inForm Tissue Finder (Akoya Biosciences). Cell detection and phenotyping were further confirmed by two pathologists. In the 36 cases studied, traditional ICC and mICC demonstrated good concordance (kappa coefficient, >0.5; p<.01) at three cutoffs (1%, 5%, and 50%), except for GATA3 at the 1% cutoff. Single-marker positivity outnumbered double-marker positivity and the exceedingly rare triple-marker positivity (<3%). GATA3 was the leading single marker-positive phenotype in all breast cancer subtypes, except for MMG in estrogen receptor-positive, progesterone receptor-positive, and human epidermal growth factor receptor 2-positive (ER+/PR+/HER2+) breast cancers. Limited to two markers, GATA3/MMG included the greatest number of tumor cells for luminal breast cancers (ER+/PR+/HER2+, 60.6%; ER+/PR+/HER2+, 31.4%), whereas HER2-overexpressed breast cancers (27.4%) and triple-negative breast cancers (26.4%) favored the combination of GATA3/GCDFP15. For a single marker, GATA3 displayed the highest sensitivity. The addition of MMG for hormone receptor-positive breast cancers and GCDFP15 for hormone receptor-negative breast cancers further increased sensitivity. The low proportion of multimarker-positive cells suggested that the coexpression observed with traditional ICC is attributable to intratumoral heterogeneity, not genuine coexpression.

Machine learning based intratumor heterogeneity signature for predicting prognosis and immunotherapy benefit in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a prevalent malignancy that is highly aggressive and heterogeneous. Intratumor heterogeneity (ITH) showed strong link to tumor progression and metastasis. High ITH may promote tumor evolution. An ITH-related signature (IRS) was created using as integrative technique including 10 machine learning methods based on TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets. The relevance of IRS in predicting the advantages of immunotherapy was assessed using a number of prediction scores and three immunotherapy datasets (GSE78220, IMvigor210 and GSE91061). Vitro experiments were performed to verify the biological functions of AKR1B1. The RSF + Enet (alpha = 0.1) projected model was proposed as the ideal IRS because it had the highest average C-index. The IRS demonstrated a strong performance in serving as an independent risk factor for the clinical outcome of STAD patients. It performed exceptionally well in predicting the overall survival rate of STAD patients, as seen by the TCGA cohort’s AUC of 1-, 3-, and 5-year ROC curves, which were 0.689, 0.683, and 0.669, respectively. A low IRS score demonstrated a superior response to immunotherapy, as seen by a lower TIDE score, lower immune escape score, greater TMB score, higher PD1&CTLA4 immunophenoscore, higher response rate, and improved prognosis. Common chemotherapeutic and targeted treatment regimens had lower IC50 values in the group with higher IRS scores. Vitro experiment showed that AKR1B1 was upregulated in STAD and knockdown of AKR1B1 obviously suppressed tumor cell proliferation and migration. The present investigation produced the best IRS for STAD, which may be applied to prognostication, risk stratification, and therapy planning for STAD patients.

Multiregional transcriptomic profiling provides improved prognostic insight in localized non-small cell lung cancer

Lung Cancer remains the leading cause of cancer deaths in the USA and worldwide. Non-small cell lung cancer (NSCLC) harbors high transcriptomic intratumor heterogeneity (RNA-ITH) that limits the reproducibility of expression-based prognostic models. In this study, we used multiregional RNA-seq data (880 tumor samples from 350 individuals) from both public (TRACERx) and internal (MDAMPLC) cohorts to investigate the effect of RNA-ITH on prognosis in localized NSCLC at the gene, signature, and tumor microenvironment levels. At the gene level, the maximal expression of hazardous genes (expression negatively associated with survival) but the minimal expression of protective genes (expression positively associated with survival) across different regions within a tumor were more prognostic than the average expression. Following that, we examined whether multiregional expression profiling can improve the performance of prognostic signatures. We investigated 11 gene signatures collected from previous publications and one signature developed in this study. For all of them, the prognostic prediction accuracy can be significantly improved by converting the regional expression of signature genes into sample-specific expression with a simple function—taking the maximal expression of hazardous genes and the minimal expression of protective genes. In the tumor microenvironment, we found a similar rule also seems applicable to immune ITH. We calculated the infiltration levels of major immune cell types in each region of a sample based on expression deconvolution. Prognostic analysis indicated that the region with the lowest infiltration level of protective or highest infiltration level of hazardous immune cells determined the prognosis of NSCLC patients. Our study highlighted the impact of RNA-ITH on the prognostication of NSCLC, which should be taken into consideration to optimize the design and application of expression-based prognostic biomarkers and models. Multiregional assays have the great potential to significantly improve their applications to prognostic stratification.

Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer

Bladder cancer is the fourth most common malignancy in men with poor prognosis. Programmed cell death (PCD) exerts crucial functions in many biological processes and immunotherapy responses of cancers. Cell death signature (CDS) is novel gene signature comprehensively considering the characteristics of 15 patterns of programmed cell death, which could affect the prognosis and immunotherapy benefits of cancer patients. Integrative machine learning procedure including 10 algorithms was conducted to construct a prognostic CDS using TCGA, GSE13507, GSE31684, GSE32984 and GSE48276 datasets. Immunophenoscore, intratumor heterogeneity (ITH), tumor immune dysfunction and exclusion (TIDE) score and five immunotherapy cohorts were used to evaluate the predictive value of CDS in immunotherapy response. The prognostic CDS constructed by StepCox[backward] + Ridge algorithms was regarded as the optimal prognostic model. The CDS had a stable and powerful performance in predicting overall survival of bladder cancer patients with the AUCs at 3-year, 5-year, and 7-year ROC of 0.740, 0.763 and 0.820 in TCGA cohort. Moreover, CDS score acted as an independent risk factor for overall survival rate of bladder cancer patients. Low CDS score had a higher abundance of immuno-activated cells, higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score, lower immune escape score, lower ITH score, lower cancer-related hallmarks score in bladder cancer. The CDS score was higher in non-responders in pan-cancer patients receiving immunotherapy. Our study constructed a novel prognostic CDS, which could serve as an indicator for predicting the prognosis in postoperative bladder cancer cases and immunotherapy benefits in pan-cancer. Low CDS score indicated a better prognosis and immunotherapy benefits.

A classical epithelial state drives acute resistance to KRAS inhibition in pancreas cancer.

Intra-tumoral heterogeneity in pancreatic ductal adenocarcinoma (PDAC) is characterized by a balance between basal and classical epithelial cancer cell states, with basal dominance associating with chemoresistance and a dismal prognosis. Targeting oncogenic KRAS, the primary driver of pancreatic cancer, shows early promise in clinical trials but efficacy is limited by acquired resistance. Using genetically engineered mouse models and patient-derived xenografts, we find that basal PDAC cells are highly sensitive to KRAS inhibitors. Employing fluorescent and bioluminescent reporter systems, we longitudinally track cell-state dynamics in vivo and reveal a rapid, KRAS inhibitor-induced enrichment of the classical state. Lineage-tracing identifies these enriched classical PDAC cells to be a reservoir for disease relapse. Genetic ablation of the classical cell-state is synergistic with KRAS inhibition, providing a pre-clinical proof-of-concept for this therapeutic strategy. Our findings motivate combining classical-state directed therapies with KRAS inhibitors to deepen responses and counteract resistance in pancreatic cancer.