A hypergraph-based model for tumor prognosis using local and global information fusion on H&E-stained histology images.

BackgroundImmune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of many cancers. However, the limited population that benefits from ICI therapy makes it necessary to screen predictive biomarkers for stratifying patients. Currently, many biomarkers, such as tumor mutational burden (TMB), have been used in the clinic as indicative biomarkers. However, some high-TMB patients with mutations in genes that are closely related to immunotherapeutic resistance are not sensitive to ICI therapy. Thus, there is a need to move beyond TMB and identify specific genetic determinants of the response to ICI therapy. In this study, we established a comprehensive mutation-based gene set across different tumor types to predict the efficacy of ICI therapy.MethodsWe constructed and validated a mutational signature to predict the prognosis of patients treated with ICI therapy. Then, the underlying immune response landscapes of different subtypes were investigated with multidimensional data.ResultsThis study included genomic and clinical data for 12,647 patients. An eleven-gene mutation-based gene set was generated to divide patients into a high-risk group and a low-risk group in a training cohort (1572 patients with 9 types of cancers who were treated with ICI therapy). Validation was performed in a validation cohort (932 patients with 5 types of cancers who were treated with ICI therapy). Mutations in these 11 genes were associated with a better response to ICI therapy. In addition, the mutation-based gene set was demonstrated to be an independent prognostic factor after ICI therapy. We further explored the role of the immune context in determining the benefits of immunotherapy in 10,143 patients with 33 types of cancers and found distinct immune landscapes for the high- and low-risk groups.ConclusionsThe mutation-based gene set developed in this study can be used to reliably predict survival benefit across cancers in patients receiving ICI therapy. The close interplay between the extrinsic and intrinsic immune landscapes in the identified patient subgroups and the subgroups’ differing responses to ICI therapy could guide immunotherapy treatment decisions for cancer patients.

Background & objective: Recently many laboratories are evaluating the usefulness of determining p53, and Ki67 proliferation activities using immunohistochemical techniques in cancer. Although the available studies suggest that these factors might help make treatment decisions in cancer patients, their clinical usefulness is still controversial. The present study was undertaken to see the expression of p53 and Ki-67 in breast carcinoma and their associations with histopathological variables. Methods: This cross-sectional analytical study was conducted in the Department of Pathology, in collaboration with the Department of Surgery, Rajshahi Medical College Hospital and the Department of Pathology, Bangobandhu Sheikh Mujib Medical University (BSMMU), Dhaka over a period of one and a half year from July 2018 to December 2019. Resected specimens of breast tissue, histopathologically confirmed as breast carcinoma, were the study population. Histological grading of breast carcinoma was done according to Nottingham modification of the Bloom-Richardson system, while staging was done using Tumor-Node-Metastasis (TNM) classification of malignant tumors, as recommended by the Union for International Cancer Control (UICC). The Ki67 and p53 expressions were assessed by immunohistochemistry. Based on the Ki67 labeling index (Ki67-LI), the patients were divided into two groups – Ki 67 > 30% (considered as over-expressed) and ≤ 30% (considered as less expressed). Likewise, p53 immuno-expression was grouped as over-expressed (score ≤ 8) and low-expressed (score > 8). The histological grade, type, and stage were then compared between patients with tumours of high and low proliferating activities to find the associations of Ki67 and p53 with tumor grade, type, and stage. Result: In the present study, almost three-quarters (74%) of the specimen of the breast had high Ki67-LI and 26% intermediate and low Ki67-LI. The p53 was found to be highly expressed in the majority (86%) of the cases. Analyses of the association of biomarkers with histological grade, stage, and type revealed that advanced-stage breast cancers (Stage-IIIA) were more likely to be associated with high Ki67-LI than the early-stage breast cancers (Stage-IIA and Stage-IIB) (p = 0.023). Poorly differentiated (G3) carcinoma also more often tends to be associated with high Ki67-LI than with low and intermediate Ki67-LI (p = 0.066). Likewise, stage IIIA tumors and poorly differentiated carcinoma were also considerably higher in the p53 over-expressed group (23.3%) than in the low p53 expression group (14.3%) (p = 0.023). Conclusion: The study concluded that a substantial proportion of breast carcinomas demonstrates high Ki67-LI. The p53 overexpression is also found in the majority (86%) of the breast carcinoma cases. Both Ki67 and p53 proliferation activities show their significant presence in high-grade and advanced-stage tumours. Ibrahim Card Med J 2023; 13 (1&2): 11-18

Objective Presenting treatment outcomes positively or negatively may differently influence treatment preferences and lead to sub-optimal decision in a medical context. This review systematically organised how positive versus negative framing of treatment outcomes influenced cancer treatment decisions of cancer patients and individuals without a cancer diagnosis. Design Three databases (PubMed, PsycInfo and Scopus) were searched for studies reporting the effects of positive versus negative framing on cancer treatment decision-making from 1981 to December 2020. Main Outcome Measure The effects of positive versus negative framing on cancer treatment preferences and the elimination of framing effect were evaluated. Results A total of 12 studies that met inclusion criteria were reviewed. Framing effect was consistently observed in individuals without a cancer diagnosis. There was not enough evidence to suggest a robust framing effect in cancer patients. Surgery was preferred in positive framing, whereas adjuvant therapy was preferred in negative framing. Justification intervention significantly eliminated framing effect. Mixed framing failed to eliminate framing effect. Conclusion Current recommendations for presenting treatment options are based on research in cancer-screening decision-making. Knowledge of how positive versus negative framing affect cancer patients’ treatment decisions is still limited. Our review highlighted the need for continued research in this area.

Medical treatment decision-making in rural cancer patients: A qualitative systematic review and meta-synthesis

A crucial point in making treatment decisions for cancer patients is the assessment of tumor aggressiveness. Currently, for breast cancer, established prognostic markers exist that are routinely assessed by standard pathological examination. However, these parameters are often not sufficient to stratify patients, especially those with early stages of breast cancer and adjuvant therapy is frequently administered to patients who might have been cured by surgery and anti-hormonal treatment alone. The goal to avoid over- and under-treatment has led to an intensive search for prognostic and predictive markers in early breast cancer. Physical interactions between cancer cells and the extracellular matrix (ECM) that occur at the molecular (nanometer) scale are crucial for the metastatic process. Consequently, nanomechanical alterations of cells and ECM due to cancer progression can serves as potentially suitable markers of cancer aggressiveness that may help to optimize treatment strategies. This motivated us to develop an atomic force microscope (AFM)-based method for measuring nanomechanical (stiffness) profiles of unadulterated tissues in native physiological buffer conditions with an unprecedented stiffness sensitivity resolved at nanometer-scale spatial resolution. An AFM utilizes a ∼10 nm-sharp stylus or tip that makes ∼10’000 miniscule indentations across tissue. In our initial study using transgenic mouse model of human breast cancer we could show that the “softest” nanomechanical phenotype (∼0.4 - 0.8 kPa) present at the primary tumor site closely corresponded to the stiffness of the metastatic lesions obtained from the lungs of the same mouse (Plodinec et. al; Nature Nanotech. 2012). In this study, we have analyzed human 187 breast cancer samples from breast biopsies and tumor resections including primary breast cancers of various stage and grade, lymph node metastases, and non-neoplastic breast parenchyma. Post-AFM samples were fixed, paraffin embedded in an oriented manner and used for routine histology. Data showed for all human breast cancer samples, distinct stiffness phenotypes in comparison to the surrounding non-neoplastic and morphologically normal breast tissue and revealed specific nanomechanical profiles of phenotypes that lead to metastases. Interestingly, patients presenting nanomechanical changes in the adjacent tissue, which was histologically characterized as tumor free, typically exhibited local and/or distant metastases. Overall, our findings demonstrate the first application of nanomechanical profiling in a clinical setting that allows for fast, on-site assessment of specimen and does not suffer from inter-observer variability such as other markers (Obermann et al, Pathologe, 2012). The relative size and distribution of nanomechanical profiles can provide an indicator of cancer aggressiveness, and therefore orientate therapy choice, and support patient follow-up. Citation Format: Marija Plodinec, Christian Raez, Philipp Oertle, Adrian Hodel, Maja Fünfschilling, Roderick YH Lim, Ellen C. Obermann. Nanomechanical profiling of breast cancer: A novel tool for cancer diagnostics and prognostics. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3926.

IntroductionMany laboratories are currently evaluating the usefulness of determination of HER2, p53, and Ki67 proliferation indices using immunohistochemical techniques in cancer. Although the available studies suggest that these factors might indeed be helpful in making treatment decisions in cancer patients, their clinical usefulness is still controversial.MethodsExpression of HER2, p53, and Ki67 was examined by immunohistochemistry in samples of breast tissue from 506 patients with invasive ductal carcinoma, obtained between 1981 and 1999 (median follow up period 82 months), and their significance for prognosis was analyzed.ResultsOf the 506 carcinoma tissue samples, 20.1%, 29.0%, and 53.6% were positive for HER2 over-expression, p53 protein accumulation, and Ki67 expression, respectively. Over-expression of HER2 significantly reduced disease free (P = 0.02) and overall survival (P = 0.005). Accumulation of p53 protein significantly decreased disease free (P = 0.01) and overall survival (P = 0.01). Patients with tumors that were positive for both HER2 and p53 relapsed and died within a significantly shorter period of time after surgery (P = 0.0001 and P < 0.0001, respectively). In multivariate analysis, patients with both HER2 and p53 positive tumors had considerably decreased overall survival (P = 0.04), as did patients with larger tumor size and positive lymph node status.ConclusionThe findings of the present study indicate that the coexistence of HER2 over-expression and p53 protein accumulation is a strong prognostic molecular marker in breast cancer.

This article will review recent impact of massively parallel next-generation sequencing (NGS) in our understanding and treatment of cancer. While whole exome sequencing (WES) remains popular and effective as a method of genetically profiling different cancers, advances in sequencing technology has enabled an increasing number of whole-genome based studies. Clinically, NGS has been used or is being developed for genetic screening, diagnostics, and clinical assessment. Though challenges remain, clinicians are in the early stages of using genetic data to make treatment decisions for cancer patients. As the integration of NGS in the study and treatment of cancer continues to mature, we believe that the field of cancer genomics will need to move toward more complete 100% genome sequencing. Current technologies and methods are largely limited to coding regions of the genome. A number of recent studies have demonstrated that mutations in non-coding regions may have direct tumorigenic effects or lead to genetic instability. Non-coding regions represent an important frontier in cancer genomics.

This paper presents a technique that can potentially help to determine the receptivity stage of the endometrium from histology images by automatically measuring the stromal nuclear changes. The presented technique is composed of an image segmentation model and the statistical evolution of segmented areas in hematoxylin and eosin (HE)-stained histology images. Three different endometrium receptivity stages, namely pre-receptive, post-receptive, and receptive, were compared. An ensemble-based AI model was proposed for histology image segmentation, which is based on individual UNet++, UNet, and ResNet34-UNet segmentation models. The performance of the ensemble-based technique was assessed using the Dice score and intersection over unit (IoU) values. In comparison to alternative segmentation architectures that were applied singly, the current ensemble-based method obtained higher Dice score (0.95) and IoU (0.90) values. The statistical comparison highlighted a noticeable difference in the number of nuclei and the size of the stroma tissue. The proposed technique demonstrated the positive potential for practical implementation for automatic endometrial tissue analysis.

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

Diagnostic Potential of lncRNAs in Cancer

Cancer occurrence rates exhibit diverse age-related patterns, and understanding them may shed new and important light on the drivers of cancer evolution. This study systematically analyzes the age-dependent occurrence rates of 23 carcinoma types, focusing on their age-dependent distribution patterns, the determinants of peak occurrence ages, and the significant difference between the two genders. According to the SEER reports, these cancer types have two types of age-dependent occurrence rate (ADOR) distributions, with most having a unimodal distribution and a few having a bimodal distribution. Our modeling analyses have revealed that (1) the first type can be naturally and simply explained using two age-dependent parameters: the total number of stem cell divisions in an organ from birth to the current age and the availability levels of bloodborne growth factors specifically needed by the cancer (sub)type, and (2) for the second type, the first peak is due to viral infection, while the second peak can be explained as in (1) for each cancer type. Further analyses indicate that (i) the iron level in an organ makes the difference between the male and female cancer occurrence rates, and (ii) the levels of sex hormones are the key determinants in the onset age of multiple cancer types. This analysis deepens our understanding of the dynamics of cancer evolution shared by diverse cancer types and provides new insights that are useful for cancer prevention and therapeutic strategies, thereby addressing critical gaps in the current paradigm of oncological research.

BACKGROUND: Immunotherapy in gliomas is limited by intratumor heterogeneity (ITH) and low mutational burden. We developed a novel comprehensive in silico pipeline for detecting tumor-specific splicing events (neojunctions), and across multiple cancer types, we successfully identified a new class of tumor-wide, public, alternatively spliced neoantigens (ASNs) that elicit CD8+ T-cell-mediated immune responses. METHODS: Our pipeline identified recurring public neojunctions expressed in TCGA RNA-seq (positive sample rate (PSR) &amp;gt; 10%) and not in GTEx normal tissue RNA-seq data (PSR &amp;lt; 1%) across ten cancer types. To characterize tumor-wide neojunctions, we utilized available multi-site RNA sequencing across diverse cancer types. With gliomas, our in-house dataset comprised of 56 patients with approximately 10 maximally-distanced intratumoral biopsy sites per patient (n=535). Tumor-wide public neojunction expression was subsequently validated in RNA-sequencing and mass spectrometry (MS) data from patient-derived cell lines (n=68) and samples (n=447). Two independent algorithms then predicted peptide processing likelihood and HLA-binding affinity of ASN candidates. TCRs identified by in vitro sensitization (IVS) of donor-derived CD8+ T-cells followed by 10x VDJ scRNA-seq, were transduced into CD8+ T-cells and co-cultured with tumor cells to evaluate TCR functionality and cytotoxicity. RESULTS: Pan-cancer analysis identified large subsets of neojunctions that were interpatiently and intratumorally conserved. Our glioma-specific analysis identified 789 public neojunctions, and 8 ASNs were validated in transcriptomic and proteomic glioma data and predicted to be presented by HLA-A*02:01 with high confidence. IVS and subsequent 10x VDJ scRNA-seq on expanded PBMC-derived CD8+ populations cultured against ASN-pulsed dendritic cells identified TCR clonotypes reactive against neojunctions in RPL22 (n=7) and GNAS (n=1), the latter being highly intratumorally-conserved (detected in &amp;gt; 90% of spatially-mapped biopsies in 17/56 patients (26.78%)). TCR-transduced T-cells demonstrated recognition and immunogenic activation against endogenously processed and presented neoantigens in both pan-cancer cell lines and transfected COS7 cells and cytotoxicity against glioma cell lines. Pan-cancer analysis revealed the detection of both neojunctions in various tumor types beyond gliomas, with the GNAS neojunction notably found tumor-wide in mesothelioma, prostate cancer, and liver hepatocellular carcinoma spatial data. CONCLUSION: Our unique integrative pipeline identified a novel class public tumor-wide splice-derived neoantigen candidates and ASN-specific TCRs, offering a promising off-the-shelf immunotherapy approach for diverse cancer types and tackling the critical challenge of ITH in immunotherapy resistance. Citation Format: Darwin Kwok, Nicholas O. Stevers, Takahide Nejo, Lee H. Chen, Inaki Etxeberria, Jangham Jung, Kaori Okada, Maggie Colton Cove, Senthilnath Lakshmanachetty, Marco Gallus, Abhilash Barpanda, Chibo Hong, Gary KL Chan, Samuel H. Wu, Emilio Ramos, Akane Yamamichi, Jerry YZ Liu, Payal Watchmaker, Hirokazu Ogino, Atsuro Saijo, Aidan Du, Nadia Grishanina, James Woo, Aaron Diaz, Susan M. Chang, Joanna J. Phillips, Arun P. Wiita, Christopher A. Klebanoff, Joseph F. Costello, Hideho Okada. Novel public and tumor-wide neoantigens arising from clonal aberrant splicing events drive tumor-specific T-cell responses across diverse cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3907.

OBJECTIVESThe aim of this study was to determine Syntax scores based on coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) and to assess whether heavy coronary calcification significantly limits the CCTA evaluation and the impact of severe calcification on heart team’s treatment decision and procedural planning in patients with three-vessel coronary artery disease (CAD) with or without left main disease.METHODSSYNTAX III was a multicentre, international study that included patients with three-vessel CAD with or without left main disease. The heart teams were randomized to either assess coronary arteries with coronary CCTA or ICA. We stratified the patients based on the presence of at least 1 lesion with heavy calcification defined as arc of calcium >180° within the lesion using CCTA. Agreement on the anatomical SYNTAX score and treatment decision was compared between patients with and without heavy calcifications.RESULTSOverall, 222 patients with available CCTA and ICA were included in this trial subanalysis (104 with heavy calcification, 118 without heavy calcification). The mean difference in the anatomical SYNTAX score (CCTA derived—ICA derived) was lower in patients without heavy calcifications [mean (−1.96 SD; +1.96 SD) = 1.5 (−19.3; 22.4) vs 5.9 (−17.5; +29.3), P = 0.004]. The agreement on treatment decision did not differ between patients with (Cohen’s kappa 0.79) or without coronary calcifications (Cohen’s kappa 0.84). The agreement on the treatment planning did not differ between patients with (concordance 80.3%) or without coronary calcifications (concordance 82.8%).CONCLUSIONSAn overall good correlation between CCTA- and ICA-derived Syntax score was found. The presence of heavy coronary calcification moderately influenced the agreement between CCTA and ICA on the anatomical SYNTAX score. However, agreement on the treatment decision and planning was high and irrespective of the presence of calcified lesions.

Half of oncologists fail to use ordered NGS results to guide their first-line treatment decision in advanced NSCLC: A retrospective study in a community-based integrated healthcare system

Background: Utilizing the separation techniques based on mass spectrometry, molecular spectrometry and various medicinal treatment drugs in the field of biomedicine can help in understanding and applying treatments for a multitude of different disease or diverse types of cancer and even expand upon efficient and reliable diagnosis in clinical settings. However, despite the use of these technologies, interpretation of the measured results is very difficult. Chemical reaction mechanisms, especially nucleophilic substitution reaction mechanism tools can be applied to accurately interpret these measurement results to determine the cause of chronic diseases and develop treatments. Aniline and its derivatives serve as important molecules in biomedicine. Understanding the nucleophilic substitution reaction mechanism of basic molecule as aniline helps to accurately understand the base reactions of DNA in our body cells. How the length of telomeres at the ends of DNA in our body’s cells increases or decreases can be understood by applying the reaction mechanism of aniline and its derivatives. Using the nucleophilic substitution reaction mechanism tools of basic molecule likely aniline and their derivatives in the field of biomedicine are available to understand and to apply also treatments for multitude of different diseases like Alzheimer’s disease and divers’ types of cancer, and to expand on efficient and reliable diagnosis in clinical treatments. Summary: This article deals with the interpretation of reaction mechanisms that can easily understand the pharmacological reaction and cellular response of biomedicine. Results: Molecular nucleophilic substitution reaction mechanism tools of basic molecules likely aniline and their derivatives in the field of biomedicine are available to understand and to apply also treatments for multitude of different diseases like Alzheimer’s disease and divers’ types of cancer, and to expand on efficient and reliable diagnosis in clinical treatments. Conclusions: By understanding the molecular mechanisms that explain drug reactions and therapeutic effects at the molecular level, it can be used for more precise tracking the causes of cancer and chronic diseases, and the reactions that occur within cells during treatment.