Iron-(Fe3+) dependent reactivation of telomerase drives colorectal cancers.

Over-consumption of iron-rich red meat and hereditary or genetic iron overload are associated with increased risk of colorectal carcinogenesis, yet the mechanistic basis of how metal-mediated signaling leads to oncogenesis remains enigmatic. Using fresh colorectal cancer (CRC) samples we identify Pirin, an iron sensor, that overcomes a rate-limiting step in oncogenesis, by re-activating the dormant human-reverse-transcriptase (hTERT) subunit of telomerase holoenzyme in an iron-(Fe3+)-dependent-manner and thereby drives CRCs. Chemical genetic screens combined with isothermal-dose response fingerprinting and mass-spectrometry identified a small molecule SP2509, that specifically inhibits Pirin-mediated hTERT reactivation in CRCs by competing with iron-(Fe3+) binding. Our findings, first to document how metal ions reactivate telomerase, provide a molecular mechanism for the well-known association between red meat, and increased incidence of CRCs. Small molecules like SP2509 represent a novel modality to target telomerase that acts as driver of 90% human cancers and is yet to be targeted in clinic.

Transcriptome Deconvolution Reveals Absence of Cancer Cell Expression Signature in Immune Checkpoint Blockade Response.

Immune checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some patients with cancer. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer- and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic expression signatures of ICB response conserved across tumor types, suggesting that cancer cells lack tissue-agnostic transcriptomic features of ICB response. SIGNIFICANCE: Our results challenge the prevailing dogma that cancer cells present tissue-agnostic molecular markers that modulate immune activity and ICB response, which has implications on the development of improved ICB diagnostics and treatments.

Use of Natural Language Processing to Infer Sites of Metastatic Disease From Radiology Reports at Scale.

PURPOSE: To evaluate natural language processing (NLP) methods to infer metastatic sites from radiology reports. METHODS: A set of 4,522 computed tomography (CT) reports of 550 patients with 14 types of cancer was used to fine-tune four clinical large language models (LLMs) for multilabel classification of metastatic sites. We also developed an NLP information extraction (IE) system (on the basis of named entity recognition, assertion status detection, and relation extraction) for comparison. Model performances were measured by F1 scores on test and three external validation sets. The best model was used to facilitate analysis of metastatic frequencies in a cohort study of 6,555 patients with 53,838 CT reports. RESULTS: The RadBERT, BioBERT, GatorTron-base, and GatorTron-medium LLMs achieved F1 scores of 0.84, 0.87, 0.89, and 0.91, respectively, on the test set. The IE system performed best, achieving an F1 score of 0.93. F1 scores of the IE system by individual cancer type ranged from 0.89 to 0.96. The IE system attained F1 scores of 0.89, 0.83, and 0.81, respectively, on external validation sets including additional cancer types, positron emission tomography-CT ,and magnetic resonance imaging scans, respectively. In our cohort study, we found that for colorectal cancer, liver-only metastases were higher in de novo stage IV versus recurrent patients (29.7% v 12.2%; P < .001). Conversely, lung-only metastases were more frequent in recurrent versus de novo stage IV patients (17.2% v 7.3%; P < .001). CONCLUSION: We developed an IE system that accurately infers metastatic sites in multiple primary cancers from radiology reports. It has explainable methods and performs better than some clinical LLMs. The inferred metastatic phenotypes could enhance cancer research databases and clinical trial matching, and identify potential patients for oligometastatic interventions.

Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.

BACKGROUND: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. METHODS: Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: "Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior." The model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. RESULTS: In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856-0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. CONCLUSION: Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care.

The cholesterol biosynthesis enzyme FAXDC2 couples Wnt/ß-catenin to RTK/MAPK signaling.

Wnts, cholesterol, and MAPK signaling are essential for development and adult homeostasis. Here, we report that fatty acid hydroxylase domain containing 2 (FAXDC2), a previously uncharacterized enzyme, functions as a methyl sterol oxidase catalyzing C4 demethylation in the Kandutsch-Russell branch of the cholesterol biosynthesis pathway. FAXDC2, a paralog of MSMO1, regulated the abundance of the specific C4-methyl sterols lophenol and dihydro-T-MAS. Highlighting its clinical relevance, FAXDC2 was repressed in Wnt/ß-catenin-high cancer xenografts, in a mouse genetic model of Wnt activation, and in human colorectal cancers. Moreover, in primary human colorectal cancers, the sterol lophenol, regulated by FAXDC2, accumulated in the cancerous tissues and not in adjacent normal tissues. FAXDC2 linked Wnts to RTK/MAPK signaling. Wnt inhibition drove increased recycling of RTKs and activation of the MAPK pathway, and this required FAXDC2. Blocking Wnt signaling in Wnt-high cancers caused both differentiation and senescence; and this was prevented by knockout of FAXDC2. Our data show the integration of 3 ancient pathways, Wnts, cholesterol synthesis, and RTK/MAPK signaling, in cellular proliferation and differentiation.

Current advances in targeted therapy for metastatic colorectal cancer - Clinical translation and future directions.

The last two decades have witnessed major breakthroughs in the development of targeted therapy for patients with metastatic colorectal cancer (mCRC), an achievement which stems largely from advances in translational research. Precision medicine is now widely practiced in routine oncological care, where systemic therapy is individualized based on clinical factors such as primary tumor sidedness, location and number of metastases, as well as molecular factors such as the RAS and BRAF mutation status, mismatch repair / microsatellite status and presence of other actionable genomic alterations in the tumor. The optimal selection of patients with RAS and BRAF-wild type (WT), left-sided primary tumor for treatment with epidermal growth factor receptor (EGFR) and chemotherapy (chemo) has markedly improved survival in the first-line setting. The pivotal trials of cetuximab in combination with BRAF/ MEK inhibitor for BRAF V600E mutant mCRC, and panitumumab with KRAS G12C inhibitor in KRAS(G12C)-mutant mCRC have been practice-changing. Anti-HER2 small molecular inhibitor, antibodies and antibody-drug conjugates have significantly improved the treatment outcome of patients with HER2 amplified mCRC. Anti-angiogenesis agents are now used across all lines of treatment and novel combinations with immune-checkpoint inhibitors are under active investigation in MSS mCRC. The non-invasive monitoring of molecular resistance to targeted therapies using Next Generation Sequencing analysis of circulating tumor-derived DNA (ctDNA) and captured sequencing of tumors have improved patient selection for targeted therapies. This review will focus on how latest advances, challenges and future directions in the development of targeted therapies in mCRC.

Bioengineered hydrogels enhance ex vivo preservation of patient-derived tumor explants for drug evaluation.

Ex vivo patient-derived tumor slices (PDTS) are currently limited by short-term viability in culture. Here, we show how bioengineered hydrogels enable the identification of key matrix parameters that significantly enhance PDTS viability compared to conventional culture systems. As demonstrated using single-cell RNA sequencing and high-dimensional flow cytometry, hydrogel-embedded PDTS tightly preserved cancer, cancer-associated fibroblast, and various immune cell populations and subpopulations in the corresponding original tumor. Cell-cell communication networks within the tumor microenvironment, including immune checkpoint ligand-receptor interactions, were also maintained. Remarkably, our results from a co-clinical trial suggest hydrogel-embedded PDTS may predict sensitivity to immune checkpoint inhibitors (ICIs) in head and neck cancer patients. Further, we show how these longer term-cultured tumor explants uniquely enable the sampling and detection of temporal evolution in molecular readouts when treated with ICIs. By preserving the compositional heterogeneity and complexity of patient tumors, hydrogel-embedded PDTS provide a valuable tool to facilitate experiments targeting the tumor microenvironment.

Functional annotation with expression validation identifies novel metastasis-relevant genes from post-GWAS risk loci in sporadic colorectal carcinomas.

BACKGROUND: Colorectal cancer (CRC) is the third highest incidence cancer and is the leading cause of cancer mortality worldwide. Metastasis to distal organ is the major cause of cancer mortality. However, the underlying genetic factors are unclear. This study aimed to identify metastasis-relevant genes and pathways for better management of metastasis-prone patients. METHODS: A case-case genome-wide association study comprising 2677 sporadic Chinese CRC cases (1282 metastasis-positive vs 1395 metastasis-negative) was performed using the Human SNP6 microarray platform and analysed with the correlation/trend test based on the additive model. SNP variants with association testing -log10 p value ≥5 were imported into Functional Mapping and Annotation (FUMA) for functional annotation. RESULTS: Glycolysis was uncovered as the top hallmark gene set. Transcripts from two of the five genes profiled, hematopoietic substrate 1 associated protein X 1 (HAX1) and hyaluronan-mediatedmotility receptor (HMMR), were significantly upregulated in the metastasis-positive tumours. In contrast to disease-risk variants, HAX1 appeared to act synergistically with HMMR in significantly impacting metastasis-free survival. Examining the subtype datasets with FUMA and Ingenuity Pathway Analysis (IPA) identified distinct pathways demonstrating sexual dimorphism in CRC metastasis. CONCLUSIONS: Combining genome-wide association testing with in silico functional annotation and wet-bench validation identified metastasis-relevant genes that could serve as features to develop subtype-specific metastasis-risk signatures for tailored management of patients with stage I-III CRC.

DARESOME enables concurrent profiling of multiple DNA modifications with restriction enzymes in single cells and cell-free DNA.

5-Methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are the most abundant DNA modifications that have important roles in gene regulation. Detailed studies of these different epigenetic marks aimed at understanding their combined effects and dynamic interconversion are, however, hampered by the inability of current methods to simultaneously measure both modifications, particularly in samples with limited quantities. We present DNA analysis by restriction enzyme for simultaneous detection of multiple epigenomic states (DARESOME), an assay based on modification-sensitive restriction digest and sequential tag ligation that can concurrently perform quantitative profiling of unmodified cytosine, 5mC, and 5hmC in CCGG sites genome-wide. DARESOME reveals the opposing roles of 5mC and 5hmC in gene expression regulation as well as their interconversion during aging in mouse brain. Implementation of DARESOME in single cells demonstrates pronounced 5hmC strand bias that reflects the semiconservative replication of DNA. Last, we showed that DARESOME enables integrative genomic, 5mC, and 5hmC profiling of cell-free DNA that uncovered multiomics cancer signatures in liquid biopsy.

COVID-19 Severity and Waning Immunity After up to 4 mRNA Vaccine Doses in 73 608 Patients With Cancer and 621 475 Matched Controls in Singapore: A Nationwide Cohort Study.

Importance: Despite patients with cancer being at risk of poor outcomes from COVID-19, there are few published studies for vaccine efficacy in this group, with suboptimal immunogenicity and waning vaccine efficacy described in small studies being a concern. Objective: To assess the incidence rate of severe COVID-19 disease outcomes associated with the number of vaccine doses received and the waning of protection over time. Design, Setting, and Participants: A prospective multicenter observational cohort study was carried out over 2 time periods (September 15, 2021, to December 20, 2021 [delta wave], and January 20, 2022, to November 11, 2022 [omicron wave]) predominated by SARS-CoV-2 delta and omicron variants, respectively. Overall, 73 608 patients with cancer (23 217 active treatment, 50 391 cancer survivors) and 621 475 controls matched by age, sex, race and ethnicity, and socioeconomic status were included. Exposure: Vaccine doses received, from zero to 4 doses, and time elapsed since last vaccine dose. Outcomes: Competing-risk regression analyses were employed to account for competing risks of death in patients with cancer. Main outcomes were incidence rate ratios (IRRs) of COVID-19 infection, hospitalization, and severe disease (defined as requirement for supplemental oxygen, intensive care, or death). The IRRs stratified by time from last vaccine dose served as indicators of waning of vaccine effectiveness over time. Results: The mean (SD) age of actively treated patients with cancer, cancer survivors, and controls were 62.7 (14.7), 62.9 (12.6), and 61.8 (14.7) years, respectively. Of 73 608 patients with cancer, 27 170 (36.9%) were men; 60 100 (81.6%) were Chinese, 7432 (10.1%) Malay, 4597 (6.2%) Indian, and 1479 (2.0%) were of other races and ethnicities. The IRRs for the 3-dose and 4-dose vs the 2-dose group (reference) for COVID-19 hospitalization and severe disease were significantly lower during both the delta and omicron waves in cancer and control populations. The IRRs for severe disease in the 3-dose group for active treatment, cancer survivors, and controls were 0.14, 0.13, and 0.07 during the delta wave and 0.29, 0.19, and 0.21 during omicron wave, respectively. The IRRs for severe disease in the 4-dose group during the omicron wave were even lower at 0.13, 0.10 and 0.10, respectively. No waning of vaccine effectiveness against hospitalization and severe disease was seen beyond 5 months after a third dose, nor up to 5 months (the end of this study's follow-up) after a fourth dose. Conclusion: This cohort study provides evidence of the clinical effectiveness of mRNA-based vaccines against COVID-19 in patients with cancer. Longevity of immunity in preventing severe COVID-19 outcomes in actively treated patients with cancer, cancer survivors, and matched controls was observed at least 5 months after the third or fourth dose.

Inferring cancer disease response from radiology reports using large language models with data augmentation and prompting.

OBJECTIVE: To assess large language models on their ability to accurately infer cancer disease response from free-text radiology reports. MATERIALS AND METHODS: We assembled 10 602 computed tomography reports from cancer patients seen at a single institution. All reports were classified into: no evidence of disease, partial response, stable disease, or progressive disease. We applied transformer models, a bidirectional long short-term memory model, a convolutional neural network model, and conventional machine learning methods to this task. Data augmentation using sentence permutation with consistency loss as well as prompt-based fine-tuning were used on the best-performing models. Models were validated on a hold-out test set and an external validation set based on Response Evaluation Criteria in Solid Tumors (RECIST) classifications. RESULTS: The best-performing model was the GatorTron transformer which achieved an accuracy of 0.8916 on the test set and 0.8919 on the RECIST validation set. Data augmentation further improved the accuracy to 0.8976. Prompt-based fine-tuning did not further improve accuracy but was able to reduce the number of training reports to 500 while still achieving good performance. DISCUSSION: These models could be used by researchers to derive progression-free survival in large datasets. It may also serve as a decision support tool by providing clinicians an automated second opinion of disease response. CONCLUSIONS: Large clinical language models demonstrate potential to infer cancer disease response from radiology reports at scale. Data augmentation techniques are useful to further improve performance. Prompt-based fine-tuning can significantly reduce the size of the training dataset.

Polygenic risk scores for the prediction of common cancers in East Asians: A population-based prospective cohort study.

Background: To evaluate the utility of polygenic risk scores (PRSs) in identifying high-risk individuals, different publicly available PRSs for breast (n=85), prostate (n=37), colorectal (n=22), and lung cancers (n=11) were examined in a prospective study of 21,694 Chinese adults. Methods: We constructed PRS using weights curated in the online PGS Catalog. PRS performance was evaluated by distribution, discrimination, predictive ability, and calibration. Hazard ratios (HR) and corresponding confidence intervals (CI) of the common cancers after 20 years of follow-up were estimated using Cox proportional hazard models for different levels of PRS. Results: A total of 495 breast, 308 prostate, 332 female-colorectal, 409 male-colorectal, 181 female-lung, and 381 male-lung incident cancers were identified. The area under receiver operating characteristic curve for the best-performing site-specific PRS were 0.61 (PGS000873, breast), 0.70 (PGS00662, prostate), 0.65 (PGS000055, female-colorectal), 0.60 (PGS000734, male-colorectal), 0.56 (PGS000721, female-lung), and 0.58 (PGS000070, male-lung), respectively. Compared to the middle quintile, individuals in the highest cancer-specific PRS quintile were 64% more likely to develop cancers of the breast, prostate, and colorectal. For lung cancer, the lowest cancer-specific PRS quintile was associated with 28-34% decreased risk compared to the middle quintile. In contrast, the HR observed for quintiles 4 (female-lung: 0.95 [0.61-1.47]; male-lung: 1.14 [0.82-1.57]) and 5 (female-lung: 0.95 [0.61-1.47]) were not significantly different from that for the middle quintile. Conclusions: Site-specific PRSs can stratify the risk of developing breast, prostate, and colorectal cancers in this East Asian population. Appropriate correction factors may be required to improve calibration. Funding: This work is supported by the National Research Foundation Singapore (NRF-NRFF2017-02), PRECISION Health Research, Singapore (PRECISE) and the Agency for Science, Technology and Research (A*STAR). WP Koh was supported by National Medical Research Council, Singapore (NMRC/CSA/0055/2013). CC Khor was supported by National Research Foundation Singapore (NRF-NRFI2018-01). Rajkumar Dorajoo received a grant from the Agency for Science, Technology and Research Career Development Award (A*STAR CDA - 202D8090), and from Ministry of Health Healthy Longevity Catalyst Award (HLCA20Jan-0022).The Singapore Chinese Health Study was supported by grants from the National Medical Research Council, Singapore (NMRC/CIRG/1456/2016) and the U.S. National Institutes of Health (NIH) (R01 CA144034 and UM1 CA182876).

Although humans contain the same genes, the sequence within these DNA sites can vary from person to person. These small variations, also known as genetic variants, can increase the risk of developing certain diseases. While each variant will only have a weak effect, if multiple variations are present the odds of developing the disease becomes significantly higher. To determine which variants are linked to a disease, researchers carry out genome-wide association studies which involve analyzing the genomes of individuals with and without the condition and comparing their genetic codes. This data is then used to calculate how different combinations of variants impact a person's chance of getting the disease, also known as a polygenic risk score. Currently, most genome-wide association studies only incorporate genetic data from people with European ancestry. Consequently, polygenic risk scores performed using this information may not accurately predict the risk of developing the disease for individuals with other ethnicities, such as people with Asian ancestry. Here, Ho et al. evaluated how well previously calculated polygenic risk scores for the four most common cancers (breast, colorectal, prostate and lung) worked on individuals of East Asian descent. The scores were tested on a dataset containing the genetic sequence, medical history, diet and activity levels of over 21,000 people living in Singapore in the 1990s. Ho et al. found that the polygenic risk scores for breast, prostate and colorectal cancer were able to predict disease risk. However, the score for lung cancer did not perform as well. The polygenic risk score for breast cancer was the most accurate, and was able to stratify individuals into distinct risk bands at an earlier age than other scores. These findings shed light on which existing polygenic risk scores will be effective at assessing cancer risk in individuals with East Asian ancestry. Indeed, Ho et al. have already incorporated the polygenic risk score for breast cancer into a pilot study screening individuals in a comparable population in Singapore. However, the polygenic risk scores tested still performed better on individuals with European ancestry, highlighting the need to address the lack of Asian representation in genome-wide association studies.

Identification of mechanism of cancer-cell-specific reactivation of hTERT offers therapeutic opportunities for blocking telomerase specifically in human colorectal cancer.

Transcriptional reactivation of hTERT is the limiting step in tumorigenesis. While mutations in hTERT promoter present in 19% of cancers are recognized as key drivers of hTERT reactivation, mechanisms by which wildtype hTERT (WT-hTERT) promoter is reactivated, in majority of human cancers, remain unknown. Using primary colorectal cancers (CRC) we identified Tert INTeracting region 2 (T-INT2), the critical chromatin region essential for reactivating WT-hTERT promoter in CRCs. Elevated ß-catenin and JunD level in CRC facilitates chromatin interaction between hTERT promoter and T-INT2 that is necessary to turn on hTERTexpression. Pharmacological screens uncovered salinomycin, which inhibits JunD mediated hTERT-T-INT2 interaction that is required for the formation of a stable transcription complex on the hTERT promoter. Our results showed for the first time how known CRC alterations, such as APC, lead to WT-hTERT promoter reactivation during stepwise-tumorigenesis and provide a new perspective for developing cancer-specific drugs.

Healthy and cancer cells harbor the same DNA sequence, but reactivation of the Human Telomerase Reverse Transcriptase (hTERT) gene is observed only in cancer cells. How does that happen was not known for over three decades of research? This study identifies a specific DNA structure that forms only in cancer cells and brings the necessary molecular machinery into the correct position to activate the hTERT gene. The detailed mechanism of hTERT activation provided in this study will be instrumental in designing cancer cell-specific hTERT inhibitors, especially since all the other ways of inhibiting telomerase failed in the clinic.

Bystander CD4+ T cells infiltrate human tumors and are phenotypically distinct.

Tumor-specific T cells likely underpin effective immune checkpoint-blockade therapies. Yet, most studies focus on Treg cells and CD8+ tumor-infiltrating lymphocytes (TILs). Here, we study CD4+ TILs in human lung and colorectal cancers and observe that non-Treg CD4+ TILs average more than 70% of total CD4+ TILs in both cancer types. Leveraging high dimensional analyses including mass cytometry, we reveal that CD4+ TILs are phenotypically heterogeneous, within each tumor and across patients. Consistently, we find different subsets of CD4+ TILs showing characteristics of effectors, tissue resident memory (Trm) or exhausted cells (expressing PD-1, CTLA-4 and CD39). In both cancer types, the frequencies of CD39- non-Treg CD4+ TILs strongly correlate with frequencies of CD39- CD8+ TILs, which we and others have previously shown to be enriched for cells specific for cancer-unrelated antigens (bystanders). Ex-vivo, we demonstrate that CD39- CD4+ TILs can be specific for cancer-unrelated antigens, such as HCMV epitopes. Overall, our findings highlight that CD4+ TILs can also recognize cancer-unrelated antigens and suggest measuring CD39 expression as a straightforward way to quantify or isolate bystander CD4+ T cells.

The distinct clinical trajectory, metastatic sites, and immunobiology of microsatellite-instability-high cancers.

Microsatellite-instability-high (MSI-H) cancers form a spectrum of solid organ tumors collectively known as Lynch Syndrome cancers, occurring not only in a subset of colorectal, endometrial, small bowel, gastric, pancreatic, and biliary tract cancers but also in prostate, breast, bladder, and thyroid cancers. Patients with Lynch Syndrome harbor germline mutations in mismatch repair genes, with a high degree of genomic instability, leading to somatic hypermutations and, therefore, oncogenesis and cancer progression. MSI-H cancers have unique clinicopathological characteristics compared to their microsatellite-stable (MSS) counterparts, marked by a higher neoantigen load, immune cell infiltration, and a marked clinical response to immune checkpoint blockade. Patients with known Lynch Syndrome may be detected early through surveillance, but some patients present with disseminated metastatic disease. The treatment landscape of MSI-H cancers, especially colorectal cancers, has undergone a paradigm shift and remains to be defined, with immune checkpoint blockade coming to the forefront of treatment strategies in the stage IV setting. We summarize in this review the clinical features of MSI-H cancers with a specific interest in the pattern of spread or recurrence, disease trajectory, and treatment strategies. We also summarize the tumor-immune landscape and genomic profile of MSI-H cancers and potential novel therapeutic strategies.

A comparative study of tumour-on-chip models with patient-derived xenografts for predicting chemotherapy efficacy in colorectal cancer patients.

Inter-patient and intra-tumour heterogeneity (ITH) have prompted the need for a more personalised approach to cancer therapy. Although patient-derived xenograft (PDX) models can generate drug response specific to patients, they are not sustainable in terms of cost and time and have limited scalability. Tumour Organ-on-Chip (OoC) models are in vitro alternatives that can recapitulate some aspects of the 3D tumour microenvironment and can be scaled up for drug screening. While many tumour OoC systems have been developed to date, there have been limited validation studies to ascertain whether drug responses obtained from tumour OoCs are comparable to those predicted from patient-derived xenograft (PDX) models. In this study, we established a multiplexed tumour OoC device, that consists of an 8 × 4 array (32-plex) of culture chamber coupled to a concentration gradient generator. The device enabled perfusion culture of primary PDX-derived tumour spheroids to obtain dose-dependent response of 5 distinct standard-of-care (SOC) chemotherapeutic drugs for 3 colorectal cancer (CRC) patients. The in vitro efficacies of the chemotherapeutic drugs were rank-ordered for individual patients and compared to the in vivo efficacy obtained from matched PDX models. We show that quantitative correlation analysis between the drug efficacies predicted via the microfluidic perfusion culture is predictive of response in animal PDX models. This is a first study showing a comparative framework to quantitatively correlate the drug response predictions made by a microfluidic tumour organ-on-chip (OoC) model with that of PDX animal models.

Heat selection enables highly scalable methylome profiling in cell-free DNA for noninvasive monitoring of cancer patients.

Genome-wide analysis of cell-free DNA methylation profile is a promising approach for sensitive and specific detection of many cancers. However, scaling such assays for clinical translation is impractical because of the high cost of whole-genome bisulfite sequencing. We show that the small fraction of GC-rich genome is highly enriched in CpG sites and disproportionately harbors most of the cancer-specific methylation signature. Here, we report on the simple and effective heat enrichment of CpG-rich regions for bisulfite sequencing (Heatrich-BS) platform that allows for focused methylation profiling in these highly informative regions. Our novel method and bioinformatics algorithm enable accurate tumor burden estimation and quantitative tracking of colorectal cancer patient's response to treatment at much reduced sequencing cost suitable for frequent monitoring. We also show tumor epigenetic subtyping using Heatrich-BS, which could enable patient stratification. Heatrich-BS holds great potential for highly scalable screening and monitoring of cancer using liquid biopsy.

Ligand-mediated PAI-1 inhibition in a mouse model of peritoneal carcinomatosis.

Peritoneal carcinomatosis (PC) present a ubiquitous clinical conundrum in all intra-abdominal malignancies. Via functional and transcriptomic experiments of ascites-treated PC cells, we identify STAT3 as a key signaling pathway. Integrative analysis of publicly available databases and correlation with clinical cohorts (n = 7,359) reveal putative clinically significant activating ligands of STAT3 signaling. We further validate a 3-biomarker prognostic panel in ascites independent of clinical covariates in a prospective study (n = 149). Via single-cell sequencing experiments, we uncover that PAI-1, a key component of the prognostic biomarker panel, is largely secreted by fibroblasts and mesothelial cells. Molecular stratification of ascites using PAI-1 levels and STAT3 activation in ascites-treated cells highlight a therapeutic opportunity based on a phenomenon of paracrine addiction. These results are recapitulated in patient-derived ascites-dependent xenografts. Here, we demonstrate therapeutic proof of concept of direct ligand inhibition of a prognostic target within an enclosed biological space.

Recommended testing algorithms for NTRK gene fusions in pediatric and selected adult cancers: Consensus of a Singapore Task Force.

The occurrence of neurotrophic tyrosine receptor kinase (NTRK) gene fusions in a wide range of tumor types presents an attractive opportunity for using a tropomyosin receptor kinase (TRK) inhibitor as cancer therapy. Recent clinical studies have demonstrated highly efficacious outcomes associated with the use of TRK inhibitors, such as larotrectinib and entrectinib in NTRK fusion-bearing cancers, in both adult and pediatric populations. While NTRK gene fusions are commonly found in some uncommon adult and pediatric malignancies, they are also found, albeit rarely, in a wide range of more common malignancies. The potential value of testing for NTRK gene fusions in practically all advanced malignancies is underpinned by the remarkable therapeutic outcomes that TRK inhibitors offer. This requirement presents practical and financial challenges in real-world oncological practice. Furthermore, different testing platforms exist to detect NTRK gene fusions, each with its advantages and disadvantages. It is, therefore, imperative to develop strategies for NTRK gene fusion testing in an attempt to optimize the use of limited tissue specimen and financial resources, and to minimize the turnaround time. A multidisciplinary task force of Singapore medical experts in both public and private sectors was convened in late 2020 to propose testing algorithms for adult colorectal tumors, sarcomas, non-small cell lung cancer, and pediatric cancers, with particular adaptation to the Singapore oncological practice. The recommendations presented here highlight the heterogeneity of NTRK-fusion positive cancers, and emphasize the need to customize the testing methods to each tumor type to optimize the workflow.