Targeting AKR1B10 by drug repurposing with epalrestat overcomes chemoresistance in non-small cell lung cancer patient-derived tumor organoids.

PURPOSE: Systemic treatments given to non-small cell lung cancer (NSCLC) patients are often ineffective due to drug resistance. In the present study, we investigated patient-derived tumor organoids (PDTOs) and matched tumor tissues from surgically treated NSCLC patients to identify drug repurposing targets to overcome resistance towards standard-of-care platinum-based doublet chemotherapy. EXPERIMENTAL DESIGN: PDTOs were established from ten prospectively enrolled non-metastatic NSCLC patients from resected tumors. PDTOs were compared with matched tumor tissues by histopathology/immunohistochemistry, whole exome and transcriptome sequencing. PDTO growths and drug responses were determined by measuring 3D tumoroid volumes, cell viability, and proliferation/apoptosis. Differential gene expression analysis identified drug-repurposing targets. Validations were performed with internal/external NSCLC patient data sets. NSCLC cell lines were used for aldo-keto reductase 1B10 (AKR1B10) knockdown studies and xenograft models to determine the intratumoral bioavailability of epalrestat. RESULTS: PDTOs retained histomorphology and pathological biomarker expression, mutational/transcriptomic signatures, and cellular heterogeneity of the matched tumor tissues. Five (50%) PDTOs were chemoresistant towards carboplatin/paclitaxel. Chemoresistant PDTOs and matched tumor tissues demonstrated overexpression of AKR1B10. Epalrestat, an orally available AKR1B10 inhibitor in clinical use for diabetic polyneuropathy, was repurposed to overcome chemoresistance of PDTOs. In vivo efficacy of epalrestat to overcome drug resistance corresponded to intratumoral epalrestat levels. CONCLUSIONS: PDTOs are efficient preclinical models recapitulating the tumor characteristics and are suitable for drug testing. AKR1B10 can be targeted by repurposing epalrestat to overcome chemoresistance in NSCLC. Epalrestat has the potential to advance to clinical trials in drug-resistant NSCLC patients due to favorable toxicity, pharmacological profile, and bioavailability.

Differences in Tumor-Associated T cell receptor repertoires between Early-Onset and Average-Onset colorectal cancer.

The incidence of colorectal cancer (CRC) among individuals younger than age 50 (early onset CRC; EOCRC) has substantially increased, yet the etiology and molecular mechanisms underlying this alarming rise remain unclear. We compared tumor-associated T cell repertoires between EOCRC and average-onset CRC (AOCRC) to uncover potentially unique immune microenvironment-related features by age of onset. Our discovery cohort included 242 patients who underwent surgical resection at Cleveland Clinic from 2000 to 2020. EOCRC was defined as age < 50 years at diagnosis (N = 126), and AOCRC as age ≥ 60 years (N = 116). T cell receptor (TCR) abundance and clonality were measured by immunosequencing of tumors. Logistic regression models were used to evaluate the associations between TCR repertoire features and age of onset, adjusting for sex, race, tumor location, and stage. Findings were replicated in 152 EOCRC and 1,984 AOCRC cases from the Molecular Epidemiology of Colorectal Cancer Study. EOCRC tumors had significantly higher TCR diversity compared to AOCRC tumors in the discovery cohort (Odds Ratio (OR):0.44, 95% Confidence Interval (CI):0.32-0.61, p < .0001). This association was also observed in the replication cohort (OR : 0.74, 95% CI : 0.62-0.89, p = .0013). No significant differences in TCR abundance were observed between EOCRC and AOCRC in either cohort. Higher TCR diversity, suggesting a more diverse intratumoral T cell response, is more frequently observed in EOCRC than AOCRC. Further studies are warranted to investigate the role of T cell diversity and the adaptive immune response more broadly in the etiology and outcomes of EOCRC.

Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation.

OBJECTIVE: Gene expression analysis through single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of gene regulation in diverse cell types, tissues, and organisms. While existing methods primarily focus on identifying cell type-specific gene expression programs (GEPs), the characterization of GEPs associated with biological processes and stimuli responses remains limited. In this study, we aim to infer biologically meaningful GEPs that are associated with both cellular phenotypes and activity programs directly from scRNA-seq data. METHODS: We applied linear CorEx, a machine-learning-based approach, to infer GEPs by grouping genes based on total correlation optimization function in simulated and real-world scRNA-seq datasets. Additionally, we utilized a transfer learning approach to project CorEx-inferred GEPs to other scRNA-seq datasets. RESULTS: By leveraging total correlation optimization, linear CorEx groups genes and demonstrates superior performance in identifying cell types and activity programs compared to similar methods using simulated data. Furthermore, we apply this same approach to real-world scRNA-seq data from the mouse dentate gyrus and embryonic colon development, uncovering biologically relevant GEPs related to cell types, developmental ages, and cell cycle programs. We also demonstrate the potential for transfer learning by evaluating similar datasets, showcasing the cross-species sensitivity of linear CorEx. CONCLUSION: Our findings validate linear CorEx as a valuable tool for comprehensively analyzing complex signals in scRNA-seq data, leading to deeper insights into gene expression dynamics, cellular heterogeneity, and regulatory mechanisms.

Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining.

Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intelligence (explainable artificial intelligence, XAI) that integrates mutational and clinical features to identify genomic signatures by repurposing the FoundationOne Companion Diagnostic (F1CDx) assay. The training data set (n = 378) consisted of subjects with recurrent and non-recurrent Stage 2 or 3 CRC retrieved from TCGA. Genomic signatures were built for identifying subgroups in Stage 2 and 3 CRC patients according to recurrence using genomic parameters and further associations with the clinical presentation. The summarization of the top-performing genomic signatures resulted in a 32-gene genomic signature that could predict tumor recurrence in CRC Stage 2 patients with high precision. The genomic signature was further validated using an independent dataset (n = 149), resulting in high-precision prognosis (AUC: 0.952; PPV = 0.974; NPV = 0.923). We anticipate that our genomic signatures and NCCN guidelines will improve recurrence predictions in CRC molecular stratification.

Colorectal Cancer-Associated Myofibroblasts Exhibit Enhanced Angiogenin Expression and Signaling via the PLXNB2 Receptor.

INTRODUCTION: Dynamic cell-cell interactions shape the tumor microenvironment to regulate tumor growth and invasiveness. Myofibroblasts are gastrointestinal stromal cells that are upregulated in the setting of colorectal cancer (CRC) and may play an important role in tumor-stromal cell communication. Angiogenin is a 14-kDa ribonuclease that regulates myofibroblast function and has been implicated in myofibroblast-CRC cell communication in mouse models. However, its role in human patients has not been well established. METHODS: Open access, annotated single-cell RNA sequencing data of paired normal human colon and CRC tissue were available in the National Center for Biotechnology Information Gene Expression Omnibus Database. We supplemented and verified these data by analyzing scRNA-seq data from an independent set of paired normal human colon and CRC tissue. CellChat was used to quantitatively infer biologically meaningful cell-cell communication networks from scRNA-seq data. PLXNB2 and α-2 actin (ACTA2) are cell surface angiogenin receptors that regulate angiogenin signaling. Ligand-receptor interactions involving angiogenin, PLXNB2, and ACTA2 were analyzed between cell populations in each sample. RESULTS: We found no difference in overall angiogenin expression comparing normal colon and CRC tissue. In normal colon tissue, myofibroblasts do not express angiogenin or the PLXNB2 receptor. In the presence of CRC, there was a striking increase in the number of myofibroblast cells within the surrounding stroma. CRC-associated myofibroblasts were characterized by a significant upregulation of both angiogenin and PLXNB2 receptor expression (P < 0.05), while no difference was seen in ACTA2. CRC cells not only use angiogenin for autocrine signaling but also communicate with myofibroblasts via the PLXNB2 receptor. CONCLUSIONS: Compared to normal human colon tissue, CRC tissue is associated with an enrichment of myofibroblasts that exhibit upregulated expression of angiogenin and the angiogenin receptor PLXNB2. CRC cells engage in autocrine signaling via angiogenin and paracrine signaling with myofibroblasts via PLXNB2. Angiogenin appears to be directly involved in tumor-stromal cell communication in human CRC tissue and may play an important role in disease progression.

Understanding common key indicators of successful and unsuccessful cancer drug trials using a contrast mining framework on ClinicalTrials.gov.

Clinical trials are essential to the process of new drug development. As clinical trials involve significant investments of time and money, it is crucial for trial designers to carefully investigate trial settings prior to designing a trial. Utilizing trial documents from ClinicalTrials.gov, we aim to understand the common characteristics of successful and unsuccessful cancer drug trials to provide insights about what to learn and what to avoid. In this research, we first computationally classified cancer drug trials into successful and unsuccessful cases and then utilized natural language processing to extract eligibility criteria information from the trial documents. To provide explainable and potentially modifiable recommendations for new trial design, contrast mining was applied to discoverhighly contrasted patterns with a significant difference in prevalence between successful (completion with advancement to the next phase) and unsuccessful (suspended, withdrawn, or terminated) groups. Our method identified contrast patterns consisting of combinations of drug categories, eligibility criteria, study organization, and study design for nine major cancers. In addition to a literature review for the qualitative validation of mined contrast patterns, we found that contrast-pattern-based classifiers using the top 200 contrast patterns as feature representations can achieve approximately 80% F1 score for eight out of ten cancer types in our experiments. In summary, aligning with the modernization efforts of ClinicalTrials.gov, our study demonstrates that understanding the contrast characteristics of successful and unsuccessful cancer trials may provide insights into the decision-making process for trial investigators and therefore facilitate improved cancer drug trial design.

Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients' outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution.

TBX21 Methylation as a Potential Regulator of Immune Suppression in CMS1 Subtype Colorectal Cancer.

Cytotoxic T lymphocyte (CTL) infiltration is associated with survival, recurrence, and therapeutic response in colorectal cancer (CRC). Immune checkpoint inhibitor (ICI) therapy, which requires CTLs for response, does not work for most CRC patients. Therefore, it is critical to improve our understanding of immune resistance in this disease. We utilized 2391 CRC patients and 7 omics datasets, integrating clinical and genomic data to determine how DNA methylation may impact survival and CTL function in CRC. Using comprehensive molecular subtype (CMS) 1 patients as reference, we found TBX21 to be the only gene with altered expression and methylation that was associated with CTL infiltration. We found that CMS1 patients with high TBX21 expression and low methylation had a significant survival advantage. To confirm the role of Tbx21 in CTL function, we utilized scRNAseq data, demonstrating the association of TBX21 with markers of enhanced CTL function. Further analysis using pathway enrichment found that the genes TBX21, MX1, and SP140 had altered expression and methylation, suggesting that the TP53/P53 pathway may modify TBX21 methylation to upregulate TBX21 expression. Together, this suggests that targeting epigenetic modification more specifically for therapy and patient stratification may provide improved outcomes in CRC.

Identification of Immuno-Targeted Combination Therapies Using Explanatory Subgroup Discovery for Cancer Patients with EGFR Wild-Type Gene.

(1) Background: Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients' heterogeneity, immune checkpoint inhibitors (ICIs) represent some the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs do not respond well, especially patients with no targetable mutations. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although matching a patient subgroup to a treatment option that can improve patients' health outcomes remains a challenging task. (2) Methods: We extended our Subgroup Discovery algorithm to identify patient subpopulations that could potentially benefit from immuno-targeted combination therapies in four cancer types: head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and skin cutaneous melanoma (SKCM). We employed the proportional odds model to identify significant drug targets and the corresponding compounds that increased the likelihood of stable disease versus progressive disease in cancer patients with the EGFR wild-type (WT) gene. (3) Results: Our pipeline identified six significant drug targets and thirteen specific compounds for cancer patients with the EGFR WT gene. Three out of six drug targets-FCGR2B, IGF1R, and KIT-substantially increased the odds of having stable disease versus progressive disease. Progression-free survival (PFS) of more than 6 months was a common feature among the investigated subgroups. (4) Conclusions: Our approach could help to better select responders for immuno-targeted combination therapies and improve health outcomes for cancer patients with no targetable mutations.

Somatic mutation variant analysis in rural, resectable non-small cell lung carcinoma patients.

Rural non-small cell lung cancer (NSCLC) patients do worse, largely related to lack of access to care. In this study, the mutational characteristics and potential for targeted therapy in rural, resectable NSCLC patients using whole exome sequencing (WES) were analyzed. WES was performed on tumor-adjacent normal pairs from rural patients undergoing resection for NSCLC. Sequencing alignment, variant-calling, annotation, and tumor mutational burden (TMB) calculations were performed using standard methods. cBioportal and OncoKB were used for comparisons of mutational frequencies and actionable targets. Thirty-four NSCLC patients underwent WES after surgical resection. The gene most frequently containing somatic variants was TP53. The median number of somatic variants was 188 (Range 11-1056), and median TMB was 3.30 (0.33-18.56) nonsynonymous mutations per Mb. Tumor stage and survival were not associated with number of variants, TMB or TP53 mutational status. Significant concordance among the most common mutations when cross-referenced to cBioportal (R = 0.78, p < 0.0001) was observed. 24% of patients had variants in actionable genes based on OncoKB annotation. In summary, we demonstrate baseline mutational frequency and establish foundations for targeted adjuvant trials in rural NSCLC patients with specific differences. Future studies must ensure to include rural patients to improve NSCLC patient outcomes.

Single Circulating-Tumor-Cell-Targeted Sequencing to Identify Somatic Variants in Liquid Biopsies in Non-Small-Cell Lung Cancer Patients.

Non-small-cell lung cancer (NSCLC) accounts for most cancer-related deaths worldwide. Liquid biopsy by a blood draw to detect circulating tumor cells (CTCs) is a tool for molecular profiling of cancer using single-cell and next-generation sequencing (NGS) technologies. The aim of the study was to identify somatic variants in single CTCs isolated from NSCLC patients by targeted NGS. Thirty-one subjects (20 NSCLC patients, 11 smokers without cancer) were enrolled for blood draws (7.5 mL). CTCs were identified by immunofluorescence, individually retrieved, and DNA-extracted. Targeted NGS was performed to detect somatic variants (single-nucleotide variants (SNVs) and insertions/deletions (Indels)) across 65 oncogenes and tumor suppressor genes. Cancer-associated variants were classified using OncoKB database. NSCLC patients had significantly higher CTC counts than control smokers (p = 0.0132; Mann-Whitney test). Analyzing 23 CTCs and 13 white blood cells across seven patients revealed a total of 644 somatic variants that occurred in all CTCs within the same subject, ranging from 1 to 137 per patient. The highest number of variants detected in ≥1 CTC within a patient was 441. A total of 18/65 (27.7%) genes were highly mutated. Mutations with oncogenic impact were identified in functional domains of seven oncogenes/tumor suppressor genes (NF1, PTCH1, TP53, SMARCB1, SMAD4, KRAS, and ERBB2). Single CTC-targeted NGS detects heterogeneous and shared mutational signatures within and between NSCLC patients. CTC single-cell genomics have potential for integration in NSCLC precision oncology.

Circulating Tumor-Macrophage Fusion Cells and Circulating Tumor Cells Complement Non-Small-Cell Lung Cancer Screening in Patients With Suspicious Lung-RADS 4 Nodules.

PURPOSE: Low-dose computed tomography (LDCT) screening of high-risk patients decreases lung cancer-related mortality. However, high false-positive rates associated with LDCT result in unnecessary interventions. To distinguish non-small-cell lung cancer (NSCLC) from benign nodules, in the present study, we integrated cellular liquid biomarkers in patients with suspicious lung nodules (lung cancer screening reporting and data system [Lung-RADS] 4). METHODS: Prospectively, 7.5 mL of blood was collected from 221 individuals (training set: 90 nonscreened NSCLC patients, 74 high-risk screening patients with no/benign nodules [Lung-RADS 1-3], and 20 never smokers; validation set: 37 patients with suspicious nodules [Lung-RADS 4]). Circulating tumor cells (CTCs), CTC clusters, and tumor-macrophage fusion (TMF) cells were identified by blinded analyses. Screening patients underwent a median of two LDCTs (range, 1-4) with a median surveillance time of 30 (range, 11-50) months. RESULTS: In the validation set of 37 Lung-RADS 4 patients, all circulating cellular biomarker counts (P < .005; Wilcoxon test) and positivity rates were significantly higher in 23 biopsy-proven NSCLC patients (CTCs: 23 of 23 [100%], CTC clusters: 6 of 23 [26.1%], and TMF cells: 15 of 23 [65.2%]) than in 14 patients with biopsy-proven benign nodules (6 of 14 [42.9%], 0 of 14 [0%], and 2 of 14 [14.3%]). On the basis of cutoff values from the training set, logistic regression with receiver operating characteristic and area under the curve analyses demonstrated that CTCs (sensitivity: 0.870, specificity: 1.0, and area under the curve: 0.989) and TMF cells (0.652; 0.880; 0.790) complement LDCT in diagnosing NSCLC in Lung-RADS 4 patients. CONCLUSION: Cellular liquid biomarkers have a potential to complement LDCT interpretation of suspicious Lung-RADS 4 nodules to distinguish NSCLC from benign lung nodules. A future prospective, large-scale, multicenter clinical trial should validate the role of cellular liquid biomarkers in improving diagnostic accuracy in high-risk patients with Lung-RADS 4 nodules.

Tumorigenic circulating tumor cells from xenograft mouse models of non-metastatic NSCLC patients reveal distinct single cell heterogeneity and drug responses.

BACKGROUND: Circulating tumor cells (CTCs) are liquid biopsies that represent micrometastatic disease and may offer unique insights into future recurrences in non-small cell lung cancer (NSCLC). Due to CTC rarity and limited stability, no stable CTC-derived xenograft (CDX) models have ever been generated from non-metastatic NSCLC patients directly. Alternative strategies are needed to molecularly characterize CTCs and means of potential future metastases in this potentially curable patient group. METHODS: Surgically resected NSCLC primary tumor tissues from non-metastatic patients were implanted subcutaneously in immunodeficient mice to establish primary tumor patient-derived xenograft (ptPDX) models. CTCs were isolated as liquid biopsies from the blood of ptPDX mice and re-implanted subcutaneously into naïve immunodeficient mice to generate liquid biopsy CTC-derived xenograft (CDX) tumor models. Single cell RNA sequencing was performed and validated in an external dataset of non-xenografted human NSCLC primary tumor and metastases tissues. Drug response testing in CDX models was performed with standard of care chemotherapy (carboplatin/paclitaxel). Blockade of MYC, which has a known role in drug resistance, was performed with a MYC/MAX dimerization inhibitor (10058-F4). RESULTS: Out of ten ptPDX, two (20%) stable liquid biopsy CDX mouse models were generated. Single cell RNA sequencing analysis revealed an additional regenerative alveolar epithelial type II (AT2)-like cell population in CDX tumors that was also identified in non-xenografted NSCLC patients' metastases tissues. Drug testing using these CDX models revealed different treatment responses to carboplatin/paclitaxel. MYC target genes and c-MYC protein were upregulated in the chemoresistant CDX model, while MYC/MAX dimerization blocking could overcome chemoresistance to carboplatin/paclitaxel. CONCLUSIONS: To overcome the lack of liquid biopsy CDX models from non-metastatic NSCLC patients, CDX models can be generated with CTCs from ptPDX models that were originally established from patients' primary tumors. Single cell analyses can identify distinct drug responses and cell heterogeneities in CDX tumors that can be validated in NSCLC metastases tissues. CDX models deserve further development and study to discover personalized strategies against micrometastases in non-metastatic NSCLC patients.

Epigenetic Regulation of Cancer Immune Cells.

The epigenetic regulation of immune response involves reversible and heritable changes that do not alter the DNA sequence. Though there have been extensive studies accomplished relating to epigenetic changes in cancer cells, recent focus has been shifted on epigenetic-mediated changes in the immune cells including T cells, Macrophages, Natural Killer cells and anti-tumor immune responses. This review compiles the most relevant and recent literature related to the role of epigenetic mechanisms including DNA methylation and histone modifications in immune cells of wide range of cancers. We also include recent research with respect to role of the most relevant transcription factors that epigenetically control the anti-tumor immune response. Finally, a statement of future direction that promises to look forward for strategies to improve immunotherapy in cancer.

Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer.

Breast cancer (BC) is the leading cause of death among female patients with cancer. Patients with triple-negative breast cancer (TNBC) have the lowest survival rate. TNBC has substantial heterogeneity within the BC population. This study utilized our novel patient stratification and drug repositioning method to find subgroups of BC patients that share common genetic profiles and that may respond similarly to the recommended drugs. After further examination of the discovered patient subgroups, we identified five homogeneous druggable TNBC subgroups. A drug repositioning algorithm was then applied to find the drugs with a high potential for each subgroup. Most of the top drugs for these subgroups were chemotherapy used for various types of cancer, including BC. After analyzing the biological mechanisms targeted by these drugs, ferroptosis was the common cell death mechanism induced by the top drugs in the subgroups with neoplasm subdivision and race as clinical variables. In contrast, the antioxidative effect on cancer cells was the common targeted mechanism in the subgroup of patients with an age less than 50. Literature reviews were used to validate our findings, which could provide invaluable insights to streamline the drug repositioning process and could be further studied in a wet lab setting and in clinical trials.

Current and Prospective Methods for Assessing Anti-Tumor Immunity in Colorectal Cancer.

Colorectal cancer (CRC) remains one of the deadliest malignancies worldwide despite recent progress in treatment strategies. Though immune checkpoint inhibition has proven effective for a number of other tumors, it offers benefits in only a small group of CRC patients with high microsatellite instability. In general, heterogenous cell groups in the tumor microenvironment are considered as the major barrier for unveiling the causes of low immune response. Therefore, deconvolution of cellular components in highly heterogeneous microenvironments is crucial for understanding the immune contexture of cancer. In this review, we assimilate current knowledge and recent studies examining anti-tumor immunity in CRC. We also discuss the utilization of novel immune contexture assessment methods that have not been used in CRC research to date.

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential.

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

Immunity, immunotherapy, and rectal cancer: A clinical and translational science review.

Rectal cancer remains a challenging disease to treat. Therapy for locally advanced rectal cancer (LARC), the most frequent presentation, has evolved to include a multimodal approach of radiation, chemotherapy, and surgery. While this approach improves local disease control, the distant recurrence rate is nearly 30% and treatment-related morbidity is substantial, thus underscoring the need for new therapeutic approaches with better efficacy and lower side effects. Immunotherapy could potentially fill this need, but its promise is not yet realized in rectal cancer. In this translational science review, we address what is known about how cytotoxic therapies shape rectal cancer immunity and potentially prime the tumor microenvironment for response to immune checkpoint inhibitors and other immunotherapies. We also address the role of current immunotherapies in colorectal cancer and highlight where novel immunotherapy approaches are currently being evaluated in LARC. Finally, we address important future directions in LARC immunotherapy including the need to define optimal therapeutic sequencing, predictive biomarkers, strategies to limit treatment-related side effects and the potential of gut microbiome manipulation to improve outcomes. In summary, this review provides a framework to guide future research and inform immunotherapy trial design so as to advance rectal cancer care.

18F-FDG PET/CT total lesion glycolysis is associated with circulating tumor cell counts in patients with stage I to IIIA non-small cell lung cancer.

BACKGROUND: In non-small cell lung cancer (NSCLC), 18F-fluoro-2-deoxy-D-glucose (18F-FDG) uptake determined by PET and presence of circulating tumor cells (CTCs) in the peripheral blood independently predict outcomes. For 18F-FDG PET/CT staging interpretation, standardized uptake values (SUVmax/avg) are routinely used in clinical reporting. The goal was to investigate whether 18F-FDG uptake measured by SUVmax/avg, but also measures of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) (MTV × SUVavg), are associated with CTCs. METHODS: Prospectively, 7.5 mL blood was drawn from NSCLC patients at the time of staging 18F-FDG PET/CT and from healthy control subjects. CTCs were identified by immunofluorescent staining (CK8/18/19pos/EpCAMpos/CD45neg/DAPIpos nucleus). 18F-FDG PET/CTs were analyzed for SUVmax, SUVavg, MTV, and TLG. RESULTS: In 16 NSCLC patients with stage I-IIIA, MTV and TLG, in contrast to SUVmax and SUVavg, were positively associated with CTCs (linear regression analysis). No CTCs were detectable in 20 healthy control subjects. CONCLUSIONS: This pilot study demonstrates that 18F-FDG PET/CT TLG correlates with CTCs in NSCLC patients without distant metastases. TLG might be a more appropriate marker for hematogenous micrometastatic potential than SUVs.