Cancer-specific Mutations Research Articles

Ultrasensitive detection of cancer-associated nucleic acids and mutations by primer exchange reaction-based signal amplification and flow cytometry

The detection of cancer-associated nucleic acids and mutations through liquid biopsy has emerged as a highly promising non-invasive approach for early cancer detection and monitoring. In this study, we report the development of primer exchange reaction (PER) based signal amplification strategy that enables the rapid, sensitive and specific detection of nucleic acids bearing cancer specific single nucleotide mutations using flow cytometry. Using micrometer size beads as support for immobilizing oligonucleotides and programmable PER assembly for target oligonucleotide recognition and fluorescence signal amplification, we demonstrated the versatile detection of target nucleic acids including KRAS oligonucleotide, fragmented mRNAs, and miR-21. Moreover, our detection system can discriminate single base mutations frequently occurred in cancer-associated genes including KRAS, PIK3CA and P53 from cell extracts and circulating tumor DNAs (ctDNAs). The detection is highly sensitive, with a limit of detection down to 27 fM without pre-amplification. In view of a clinical application, we demonstrate the detection of single mutations after extraction and pre-amplification of ctDNAs from the plasma of breast cancer patients. Importantly, our detection strategy enabled the detection of single KRAS mutation even in the presence of 1000-fold excess of wild type (WT) DNA using multi-color flow cytometry detection approach. Overall, our strategy holds immense potential for clinical applications, offering significant improvements for early cancer detection and monitoring.

Predicting Survival Among Colorectal Cancer Patients: Development and Validation of Polygenic Survival Score.

Colorectal cancer is the second leading cause of cancer-related death in the United States. A multi-omics approach has contributed in identifying various cancer-specific mutations, epigenetic alterations, and cells response to chemotherapy. This study aimed to determine the factors associated with colorectal cancer survival and develop and validate a polygenic survival scoring system (PSS) using a multi-omics approach. Data were obtained from the Cancer Genome Atlas (TCGA). Colon Adenocarcinoma (TCGA-COAD) data were used to develop a survival prediction model and PSS, whereas rectal adenocarcinoma (TCGA-READ) data were used to validate the PSS. Cox proportional hazards regression analysis was conducted to examine the association between the demographic characteristics, clinical variables, and mRNA gene expression. Overall accuracy of PSS was also evaluated. The median overall survival for TCGA-COAD patients was 7 years and for TCGA-READ patients was 5 years. The multivariate Cox proportional hazards model identified age, cancer stage, and expression of nine genes as predictors of colon cancer survival. Based on the median PSS of 0.38, 48% of TCGA-COAD patients had high mortality risk. Patients in the low risk group had significantly higher 5-year survival rates than those in the high group (p <0.0001). The PSS demonstrated a high overall accuracy in predicting colorectal cancer survival. This study integrated clinical and transcriptome data to identify survival predictors in patients with colorectal cancer. PSS is an accurate and valid measure for estimating colorectal cancer survival. Thus, it can serve as an important tool for future colorectal cancer research.

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nuclear architecture and transcription. CTCF binding sites (CTCF-BSs) reside in non-coding DNA and are frequently mutated in cancer. Our previous study identified a small subclass of CTCF-BSs that are resistant to CTCF knock down, termed persistent CTCF binding sites (P-CTCF-BSs). P-CTCF-BSs show high binding conservation and potentially regulate cell-type constitutive 3D chromatin architecture. Here, using ICGC sequencing data we made the striking observation that P-CTCF-BSs display a highly elevated mutation rate in breast and prostate cancer when compared to all CTCF-BSs. To address whether P-CTCF-BS mutations are also enriched in other cell-types, we developed CTCF-INSITE-a tool utilising machine learning to predict persistence based on genetic and epigenetic features of experimentally-determined P-CTCF-BSs. Notably, predicted P-CTCF-BSs also show a significantly elevated mutational burden in all 12 cancer-types tested. Enrichment was even stronger for P-CTCF-BS mutations with predicted functional impact to CTCF binding and chromatin looping. Using in vitro binding assays we validated that P-CTCF-BS cancer mutations, predicted to be disruptive, indeed reduced CTCF binding. Together this study reveals a new subclass of cancer specific CTCF-BS DNA mutations and provides insights into their importance in genome organization in a pan-cancer setting.

Advancing cancer MRD monitoring through tumor whole-genome informed, duplex ctDNA sequencing.

e15044 Background: Circulating tumor DNA (ctDNA), a cell-free DNA (cfDNA) present in the plasma of cancer patients, originates from cancer cells and serves as a crucial biomarker during cancer treatment. Despite its importance, the current limit of detection (LoD) of conventional ctDNA assays is suboptimal (0.01%; 10-4) for detecting ctDNA from microscopic residual and recurrent cancer tissues. To address this, we integrated two cutting-edge techniques: whole-genome sequencing (WGS) of primary cancer tissue (a mutation capture step) and duplex DNA sequencing-based cfDNA sequencing (mutation recapture steps). Methods: Thirteen colorectal cancer patients participated in this study. Primary tumor tissues and matched normal blood tissues were collected for WGS in the mutation capture step, while plasma samples for cfDNA sequencing in the mutation recapture steps were obtained just prior to colorectal cancer surgery and 1-month post-surgery follow-up. Tumor DNA and germline DNA were analyzed using CancerVision, a CLIA-certified clinical-grade cancer WGS platform, in the mutation capture step. In the mutation recapture steps, somatically acquired mutations from the previous step were tracked in cfDNA samples using the Concatenating Original Duplex for Error Correction (CODEC) technique, a duplex DNA sequencing technique that examines both Watson and Crick strands of double-stranded DNA molecules to discern true mutations from sequencing noise. Results: The median WGS depth of tumor and germline tissues was 40x and 20x, respectively. The mean depth of cfDNA WGS with CODEC was 25x, with a median duplex sequencing rate of 70%. Overall, 4,828-301,434 somatic base substitutions (SBSs) per sample were discovered in the capture step. Four of the thirteen tumors exhibited hypermutator characteristics with microsatellite instability features. The CODEC technique demonstrated excellent capability in tracing cancer-specific mutations in cfDNA sequencing with a technical sequencing error rate of 4 x 10-7, defining the maximum technical LoD, which is 250-fold lower errors than conventional sequencing. In our sample cohort, tumor fractions in cfDNA sequencing were robustly estimated from 0.001% (10-5), below the LoD of conventional MRD methods. Conclusions: Our integration of tumor-WGS-informed duplex cfDNA sequencing methods significantly enhances the sensitivity of sequencing-based MRD assays, achieving a technical LoD limit of 10-7 for detecting ctDNA in cancer patients. These approaches represent a breakthrough in monitoring MRD in real-world cancer patients.

Revolutionizing personalized cancer treatment: the synergy of next-generation sequencing and CRISPR/Cas9.

In the context of cancer heterogeneity, the synergistic action of next-generation sequencing (NGS) and CRISPR/Cas9 plays a promising role in the personalized treatment of cancer. NGS enables high-throughput genomic profiling of tumors and pinpoints specific mutations that primarily lead to cancer. Oncologists use this information obtained from NGS in the form of DNA profiling or RNA analysis to tailor precision strategies based on an individual's unique molecular signature. Furthermore, the CRISPR technique enables precise editing of cancer-specific mutations, allowing targeted gene modifications. Harnessing the potential insights of NGS and CRISPR/Cas9 heralds a remarkable frontier in cancer therapeutics with unprecedented precision, effectivenessand minimal off-target effects.

Abstract LB097: Sensitive detection of central nervous system tumors using a sequencing based cerebrospinal fluid test

Abstract The current approach to diagnosing and monitoring tumors of the central nervous system relies almost exclusively on radiographic imaging and neurosurgical procedures. Cerebrospinal fluid (CSF) is appealing for diagnosis because it is already part of the standard of care for the diagnosis or management of several types of CNS disease, including cancer, and the tumor DNA is more highly concentrated inside the blood brain barrier than in plasma or other bodily fluids. Even though cytology is widely used, sensitivity remains low ranging from 2% to 50%, depending on cancer type. We report an analytic technique that efficiently introduces identical molecular barcodes to both strands of CSF template DNA molecules for the identification of cancer specific somatic mutations and copy number alterations. We apply this approach to 126 CSF samples obtained from individuals with known primary or metastatic tumors involving the brain and 36 CSF samples obtained from individuals with non-neoplastic neurological conditions such as multiple sclerosis and another neurologic conditions. We correctly detect 91% of cancers at a specificity of 94.5%. In cases (n=20) where standard of care cytology was available, cytology detected 25% of cancers while our molecular assay detected 90%. Our approach has the capacity to be used in combination with other clinical, radiologic, and laboratory-based data to inform the diagnosis and management of patients with suspected cancers of the brain. Positives n= Sensitivity GBM 42 46 91.3% Lymphoma 22 24 91.7% Medulloblastoma 14 16 87.5% Metastases 37 40 92.5% Citation Format: Christopher B. Douville, Chetan Bettegowda. Sensitive detection of central nervous system tumors using a sequencing based cerebrospinal fluid test [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB097.

Pan-cancer analyses suggest kindlin-associated global mechanochemical alterations

Kindlins serve as mechanosensitive adapters, transducing extracellular mechanical cues to intracellular biochemical signals and thus, their perturbations potentially lead to cancer progressions. Despite the kindlin involvement in tumor development, understanding their genetic and mechanochemical characteristics across different cancers remains elusive. Here, we thoroughly examined genetic alterations in kindlins across more than 10,000 patients with 33 cancer types. Our findings reveal cancer-specific alterations, particularly prevalent in advanced tumor stage and during metastatic onset. We observed a significant co-alteration between kindlins and mechanochemical proteome in various tumors through the activation of cancer-related pathways and adverse survival outcomes. Leveraging normal mode analysis, we predicted structural consequences of cancer-specific kindlin mutations, highlighting potential impacts on stability and downstream signaling pathways. Our study unraveled alterations in epithelial–mesenchymal transition markers associated with kindlin activity. This comprehensive analysis provides a resource for guiding future mechanistic investigations and therapeutic strategies targeting the roles of kindlins in cancer treatment.

CRISPR/Cas9 targeting of passenger single nucleotide variants in haploinsufficient or essential genes expands cancer therapy prospects

CRISPR/Cas9 technology has effectively targeted cancer-specific oncogenic hotspot mutations or insertion–deletions. However, their limited prevalence in tumors restricts their application. We propose a novel approach targeting passenger single nucleotide variants (SNVs) in haploinsufficient or essential genes to broaden therapeutic options. By disrupting haploinsufficient or essential genes through the cleavage of DNA in the SNV region using CRISPR/Cas9, we achieved the selective elimination of cancer cells without affecting normal cells. We found that, on average, 44.8% of solid cancer patients are eligible for our approach, a substantial increase compared to the 14.4% of patients with CRISPR/Cas9-applicable oncogenic hotspot mutations. Through in vitro and in vivo experiments, we validated our strategy by targeting a passenger mutation in the essential ribosomal gene RRP9 and haploinsufficient gene SMG6. This demonstrates the potential of our strategy to selectively eliminate cancer cells and expand therapeutic opportunities.

Abstract 4242: Preserving lung cancer patient mutations in 3D organoid models: Advancing cancer research through innovative culture systems

Abstract Lung cancer is one of the most prevalent cancers worldwide. There are currently many anticancer drugs available, but they have numerous issues in terms of treatment, such as drug resistance and recurrence. Culturing systems that can replace a patient's cancer tissue are crucial, and the existing 2D culturing systems have many limitations. Patient-derived cancer organoid culture is the most advanced culture system for understanding mechanism of cancers. Using a 3D organoid culture system, we have successfully established a culture system for lung cancer using biopsy/surgical specimens and malignant effusions, including a long-term culture system. There are two critical indicators to determine the success of 3D organoid culture. One is whether the organoids retain their cancer-specific mutations from patient’s lung cancer tissue, and the second is whether the organoids grow well and can be maintained in long-term culture for more than 20-30 passages. We typically assess gene mutations in EGFR, ALK, ROS, and P53 when evaluating lung cancer organoids. We confirm these mutations when we establish organoids. and currently we have established a total of 127 patient-derived lung cancer organoids. Among the 127 organoids, we completed NGS analysis on 108 of them and validated that 86.5% exhibit similarity when compared to lung cancer tissues. Additionally, we achieved a 100% retention of mutations in the long-term culturing of these organoids. Our studies indicate that patient-derived lung cancer organoids represent a meaningful organoid system for cancer tissue replacement and treatment response in the context of precision cancer medicine. Citation Format: Ji-yeon Park, Seo Rin Gu, Jiyeon Lee, Na-Gyeong Lee, Dojin Choi, Sang Eun Park, Ji Min Kim, Jinguen Rheey. Preserving lung cancer patient mutations in 3D organoid models: Advancing cancer research through innovative culture systems [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 4242.

Abstract 6968: Extracellular vesicles as carriers of signaling factors in cancer

Abstract Extracellular vesicles (EVs) are small, membrane-bound structures released by cells. They contain various biomolecules and have emerged as mediators of cellular signaling. EVs of various origins can be isolated from body fluids and hold potential as novel biomarkers. However, their clinical implementation as biomarkers is hampered by complex isolation procedures, limited expertise in routine labs, and a lack of mechanistic understanding of signal transfer, particularly in vivo. To better understand how EVs carry oncogenic signaling factors, we performed mechanistic investigations into the role of EVs in WNT signaling as a representative cancer-associated signaling pathway, as well as ex vivo validation experiments analyzing patient-derived EVs. Hydrophobic WNT ligands can use EVs to travel the extracellular space. However, little is known about the quantities of WNT secretion onto different EV fractions and potentially other vehicles. To explore WNT secretory routes and potential functional differences, we generated fluorescently tagged marker constructs of the non-canonical ligand WNT5a, which has context-dependent tumor-suppressing or -promoting functions in cancer. In additional in vitro experiments, we tested how the WNT signaling activation state and cancer-specific WNT mutations, such as CRISPR/Cas9-induced APC truncations, quantitatively and qualitatively affect EV shedding in colorectal cancer cell lines. Further functional testing of EV-bound vs. non-EV WNT ligands is ongoing. For proof-of-concept analyses and to test EVs as biomarkers, we set up the liquid biopsy study EValuate. The project aims for cross-entity analysis of EV profiles and correlation of results to cancer presence, stage, and survival. For quality control and before pipeline implementation, we tested various EV isolation methods and compared different sample pre-processing steps. By combining multidisciplinary expertise in biophysics, molecular biology, and basic and clinical cancer research, we aim to gain a better mechanistic understanding of EV-mediated signal transfer and facilitate the integration of EVs as diagnostic and prognostic biomarkers into clinical studies. Citation Format: Antonia Schubert, Nadine Winkler, Ajaree Mongkolsittisilp, Matthias Schulz, Oksana Voloshanenko, Meike Schaffrinski, Dominique Kranz, Karin Nienhaus, Dirk Jäger, Lorenz Trümper, Judith Büntzel, Claudia Binder, Gerd Ulrich Nienhaus, Michael Boutros. Extracellular vesicles as carriers of signaling factors in cancer [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 6968.

Abstract 5251: RNA primed SMAR-T cells against multiple driver mutations, all HLA's designed for first line therapy

Abstract Success of immune checkpoint inhibitors (e.g., anti-PD1 antibodies) have revolutionized cancer immunotherapy by demonstrating that a patient’s own T-cells recognize and treat cancer. The efficacy of PD-1 blockade is driven by recruitment of new T-cells from blood rather than via activation of pre-existing tumor infiltrating lymphocytes (Yost K.E., et al. Nat. Med. 2019). However, anti-PD1 therapy is most effective in ~5% of malignancies i.e., cancers with high mutational burden. Hence, the challenge of addressing most lower mutational burden cancers (the ~95%) needs an alternate treatment strategy. We address this challenge by using RNA to prime and expand peripheral T-cells to cancer-specific mutations ex-vivo. Using our proprietary, patented, and robust manufacturing method, we can generate T-cell populations reactive to as low as 8 and as high as 40 cancer-specific mutant proteins. In-vitro, these T-cells have cancer mutation-specific cytotoxicity and do not kill the normal cells. Further, the T-cells express homing receptors enabling them to infiltrate tumors and express high levels of TNFα and IFNγ, which are associated with effective tumor cytotoxicity and pro-inflammatory modification of tumor microenvironment (TME). Additional characterization shows these cells to be predominantly CD4+ and CD8+ T-cells bearing central and effector memory phenotypic with negligible regulatory or exhausted T-cells. We believe our T-cells can be used for cellular therapy in conjunction with, or as an alternative to, immune checkpoint inhibitors to treat lower mutational burden cancers present in most patients. Citation Format: Alfred E. Slanetz, Sriram Srivatsa, Farzona Muzaffar, Walter Barry, David Ryan, Salim Metri, Michael Slanetz. RNA primed SMAR-T cells against multiple driver mutations, all HLA's designed for first line therapy [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 5251.

Abstract 3695: Monitoring response to immunotherapy using cell free DNA fragmentomes

Abstract BACKGROUND: The rapid detection of disease progression in patients receiving immune checkpoint inhibitors (ICIs) is challenging since there are no reliable biomarkers of clinical response. Liquid biopsy assays may address this unmet need by using cell-free DNA (cfDNA) to monitor treatment responses. Current targeted next-generation sequencing cfDNA assays are costly and have limited assay sensitivity for detection of cancer-specific mutations at low mutant allele frequency (MAF). Here, we demonstrate the utility of DELFI Tumor Fraction (DELFI-TF), a tumor- and mutation-independent cfDNA fragmentome approach to monitor treatment response in patients with metastatic non-small cell lung cancer (mNSCLC). METHODS: Overall, 422 longitudinal blood samples were collected from 141 mNSCLC patients of which 109 were treated with immunotherapy and 32 with targeted therapy (TT). Plasma-derived cfDNA was processed with whole genome sequencing at low coverage (~4x). Circulating tumor burden was quantified as the maximum MAF (maxMAF) of tumor-derived variants detected using a 500+gene panel. Matched white blood cells were used to filter out germline and clonal hematopoietic variants. A random forest regression model was trained with maxMAF as the response using fragmentation-derived features. The accuracy of DELFI-TF was assessed using leave one patient out cross-validation in the ICI cohort and holdout validation in the cohort of patients receiving TT. For survival analyses, we used the median baseline scores to define low/high DELFI-TF and maxMAF. RESULTS: In the ICI cohort, DELFI-TF scores were strongly correlated with the maxMAF (r=0.94, p&amp;lt;0.001, Pearson). At baseline, DELFI-TF and maxMAF both differentiate responding from non-responding tumors (p = 0.01 and p = 0.001 for DELFI-TF and maxMAF respectively; Wilcoxon). Patients with high DELFI-TF or maxMAF had significantly shorter progression-free survival (PFS) compared to low DELFI-TF or maxMAF (17.0 vs 4.7 months, p &amp;lt; 0.001; 17.0 vs 4.6 months, p &amp;lt; 0.001; Log-Rank respectively) and similar overall survival (OS) (DELFI-TF high 11.4 vs low 34.1 months, p&amp;lt;0.001; maxMAF high 10.9 vs low 34.1 months, p &amp;lt; 0.001, Log-Rank). Changes in DELFI-TF and maxMAF at the earliest timepoint were highly correlated (n=109; r=0.90; Pearson). In the validation cohort of TT patients, DELFI-TF at baseline outperformed maxMAF in distinguishing patients with PFS and OS benefits (DELFI-TF PFS 36.4 vs 11.5 months, p = 0.03; maxMAF PFS 21.9 vs 15.9 months, p=0.6; Log-Rank) (DELFI-TF OS high 37 months vs low not reached (NR), p &amp;lt; 0.05; Max MAF OS high 37 vs NR, p = 0.1, Log-Rank). The majority of TT patients (81%) had consistent changes with DELFI-TF and maxMAF from baseline to the earliest timepoint. CONCLUSIONS: DELFI-TF is a tumor and mutation independent approach that is cost-efficient with high performance comparable to current ctDNA assays for estimating tumor burden and monitoring response in cancer patients. Citation Format: Bahar Alipanahi, Lavanya Sivapalan, Jamie Medina, Zachary L. Skidmore, Paola Ghanem, Erica Peters, Gavin Pereira, Nisha Rao, Kavya Velliangiri, Alissa Konicki, Stephen Cristiano, Laurel Millberg, Jacob Carey, Keith Lumbard, Noushin Niknafs, Bryan Chesnick, Jennifer Tom, Alessandro Leal, Benjamin Levy, Patrick Forde, Peter Bach, Nicholas C. Dracopoli, Robert B. Scharpf, Victor Velculescu, Lorenzo Rinaldi, Valsamo Anagnostou. Monitoring response to immunotherapy using cell free DNA fragmentomes [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 3695.

Pediatric Tumors as Disorders of Development: The Case for In Vitro Modeling Based on Human Stem Cells.

Despite improvements in patient outcomes, pediatric cancer remains a leading cause of non-accidental death in children. Recent genetic analysis of patients with pediatric cancers indicates an important role for both germline genetic predisposition and cancer-specific somatic driver mutations. Increasingly, evidence demonstrates that the developmental timepoint at which the cancer cell-of-origin transforms is critical to tumor identity and therapeutic response. Therefore, future therapeutic development would be bolstered by the use of disease models that faithfully recapitulate the genetic context, cell-of-origin, and developmental window of vulnerability in pediatric cancers. Human stem cells have the potential to incorporate all of these characteristics into a pediatric cancer model, while serving as a platform for rapid genetic and pharmacological testing. In this review, we describe how human stem cells have been used to model pediatric cancers and how these models compare to other pediatric cancer model modalities.

Liver cancer-specific mutations in functional domains of ADAR2 lead to the elevation of coding and non-coding RNA editing in multiple tumor-related genes.

Mutation is the major cause of phenotypic innovations. Apart from DNA mutations, the alteration on RNA such as the ADAR-mediated A-to-I RNA editing could also shape the phenotype. These two layers of variations have not been systematically combined to study their collective roles in cancers. We collected the high-quality transcriptomes of ten hepatocellular carcinoma (HCC) and the matched control samples. We systematically identified HCC-specific mutations in the exonic regions and profiled the A-to-I RNA editome in each sample. All ten HCC samples had mutations in the CDS of ADAR2 gene (dsRNA-binding domain or catalytic domain). The consequence of these mutations converged to the elevation of ADAR2 efficiency as reflected by the global increase of RNA editing levels in HCC. The up-regulated editing sites (UES) were enriched in the CDS and UTR of oncogenes and tumor suppressor genes (TSG), indicating the possible roles of these target genes in HCC oncogenesis. We present the mutation-ADAR2-UES-oncogene/TSG-HCC axis that explains how mutations at different layers would finally lead to abnormal phenotype. In the light of central dogma, our work provides novel insights into how to fully take advantage of the transcriptome data to decipher the consequence of mutations.

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network

BackgroundIn cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks. Here we propose a new network-based method, Net-DMPred, to predict driver missense mutations considering molecular networks. Net-DMPred consists of the graph part and the prediction part. In the graph part, molecular networks are learned by a graph neural network (GNN). The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part.ResultsNet-DMPred, which considers molecular networks, performed better than conventional methods. Furthermore, the prediction performance differed by the molecular network structure used in learning, suggesting that it is important to consider not only the local network related to cancer but also the large-scale network in living organisms.ConclusionsWe propose a network-based machine learning method, Net-DMPred, for predicting cancer driver missense mutations. Our method enables us to consider the entire graph architecture representing the molecular network because it uses GNN. Net-DMPred is expected to detect driver mutations from a lot of missense mutations that are not known to be associated with cancer.

KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality.

Synthetic lethality (SL) is a promising strategy for anticancer therapy, as inhibiting SL partners of genes with cancer-specific mutations can selectively kill the cancer cells without harming the normal cells. Wet-lab techniques for SL screening have issues like high cost and off-target effects. Computational methods can help address these issues. Previous machine learning methods leverage known SL pairs, and the use of knowledge graphs (KGs) can significantly enhance the prediction performance. However, the subgraph structures of KG have not been fully explored. Besides, most machine learning methods lack interpretability, which is an obstacle for wide applications of machine learning to SL identification. We present a model named KR4SL to predict SL partners for a given primary gene. It captures the structural semantics of a KG by efficiently constructing and learning from relational digraphs in the KG. To encode the semantic information of the relational digraphs, we fuse textual semantics of entities into propagated messages and enhance the sequential semantics of paths using a recurrent neural network. Moreover, we design an attentive aggregator to identify critical subgraph structures that contribute the most to the SL prediction as explanations. Extensive experiments under different settings show that KR4SL significantly outperforms all the baselines. The explanatory subgraphs for the predicted gene pairs can unveil prediction process and mechanisms underlying synthetic lethality. The improved predictive power and interpretability indicate that deep learning is practically useful for SL-based cancer drug target discovery. The source code is freely available at https://github.com/JieZheng-ShanghaiTech/KR4SL.

New Biomarkers and Treatment Advances in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer lacking hormone receptor expression and HER2 gene amplification. TNBC represents a heterogeneous subtype of breast cancer, characterized by poor prognosis, high invasiveness, high metastatic potential, and a tendency to relapse. In this review, the specific molecular subtypes and pathological aspects of triple-negative breast cancer are illustrated, with particular attention to the biomarker characteristics of TNBC, namely: regulators of cell proliferation and migration and angiogenesis, apoptosis-regulating proteins, regulators of DNA damage response, immune checkpoints, and epigenetic modifications. This paper also focuses on omics approaches to exploring TNBC, such as genomics to identify cancer-specific mutations, epigenomics to identify altered epigenetic landscapes in cancer cells, and transcriptomics to explore differential mRNA and protein expression. Moreover, updated neoadjuvant treatments for TNBC are also mentioned, underlining the role of immunotherapy and novel and targeted agents in the treatment of TNBC.

Cell-free DNA fragmentation profiling for therapeutic response monitoring in metastatic colorectal cancer.

e15664 Background: Currently available circulating cell-free DNA (cfDNA) assays require deep-targeted sequencing to detect cancer-specific mutations at low mutant allele frequency (MAF) levels in the blood. Recently, we developed a tumor-agnostic, mutation-independent approach that utilizes low-coverage whole genome sequencing called DELFI (DNA evaluation of fragments for early interception) Tumor Fraction (DELFI-TF), a model designed to predict plasma tumor fractions based on genome-wide fragmentation-related features. Here, we report the results of DELFI-TF applied to a prospective cohort of patients with metastatic colorectal cancer (mCRC). Methods: Overall, 692 longitudinal plasma samples collected from 153 initially treatment-naive mCRC patients participating in the phase III CAIRO5 study (NCT02162563) were sequenced at low coverage and used for training and cross-validation. In patients with tumor-tissue-proven RAS/BRAF mutations, the tumor fractions were quantified as the cfDNA MAF of the RAS/BRAF variant measured by droplet digital PCR (ddPCR). Using fragment-sequencing statistics, a Bayesian regression model was trained against the MAF of the tumor-specific driver RAS/BRAF variant in all longitudinal timepoints to generate DELFI-TF scores. Changes in longitudinal DELFI-TF scores during first-line therapy (DELFI-TF slopes) were examined to predict treatment response and survival outcomes. Results: The DELFI-TF scores were strongly correlated with RAS/BRAF cfDNA MAF measured by ddPCR ( Pearson, r= 0.85, p&lt; 0.001). Baseline DELFI-TF correlated with dimensions of liver metastases reported in CT scans ( Pearson, r= 0.49, p&lt; 0.001) as well as clinical response, with pre-treatment levels significantly lower in patients with a later-confirmed partial or complete response ( Wilcoxon, p&lt; 0.05). Patients with low or negative DELFI-TF slopes presented with longer progression-free survival in the overall study population (13.4 months vs 10.4 months, HR = 2.03, 95% CI 1.25 to 3.32, Log-rank p&lt; 0.01) and in patients who experienced durable clinical benefit (16.7 months vs 13.3 months, HR = 2.24, 95% CI 1.1 to 4.55, Log-rank p= 0.023). Patients with low or negative DELFI-TF slopes also experienced significantly longer overall survival (59.4 months vs 29.1 months, HR = 3.05, 95% CI 1.58-5.90, Log-rank p&lt; 0.001). Tissue-informed focal and arm-level copy number changes were detected 4-12 weeks after liver metastases resection. Most patients detected as having a molecular relapse were diagnosed earlier than clinical recurrences identified by conventional imaging. Conclusions: DELFI-TF demonstrates the ability to use cfDNA fragmentomes to estimate cfDNA tumor burden with performance comparable to standard approaches for treatment response monitoring and clinical outcome prediction.

FBXW7β loss-of-function enhances FASN-mediated lipogenesis and promotes colorectal cancer growth

Continuous de novo fatty acid synthesis is required for the biosynthetic demands of tumor. FBXW7 is a highly mutated gene in CRC, but its biological functions in cancer are not fully characterized. Here, we report that FBXW7β, a FBXW7 isoform located in the cytoplasm and frequently mutated in CRC, is an E3 ligase of fatty acid synthase (FASN). Cancer-specific FBXW7β mutations that could not degrade FASN can lead to sustained lipogenesis in CRC. COP9 signalosome subunit 6 (CSN6), an oncogenic marker of CRC, increases lipogenesis via interacting with and stabilizing FASN. Mechanistic studies show that CSN6 associates with both FBXW7β and FASN, and antagonizes FBXW7β’s activity by enhancing FBXW7β autoubiquitination and degradation, which in turn prevents FBXW7β-mediated FASN ubiquitination and degradation, thereby regulating lipogenesis positively. Both CSN6 and FASN are positively correlated in CRC, and CSN6-FASN axis, regulated by EGF, is responsible for poor prognosis of CRC. The EGF-CSN6-FASN axis promotes tumor growth and implies a treatment strategy of combination of orlistat and cetuximab. Patient-derived xenograft experiments prove the effectiveness of employing orlistat and cetuximab combination in suppressing tumor growth for CSN6/FASN-high CRC. Thus, CSN6-FASN axis reprograms lipogenesis to promote tumor growth and is a target for cancer intervening strategy in CRC.

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning

Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we collected the data for experimentally known driver mutations in 22 different cancer types and classified them into six categories: breast cancer (BRCA), acute myeloid leukaemia (LAML), endometrial carcinoma (EC), stomach cancer (STAD), skin cancer (SKCM), and other cancer types which contains 5747 disease prone and 5514 neutral sites in 516 proteins. The analysis of amino acid distribution along mutant sites revealed that the motifs AAA and LR are preferred in disease-prone sites whereas QPP and QF are dominant in neutral sites. Further, we developed a method using deep neural networks to predict disease-prone sites with amino acid sequence-based features such as physicochemical properties, secondary structure, tri-peptide motifs and conservation scores. We obtained an average AUC of 0.97 in five cancer types BRCA, LAML, EC, STAD and SKCM in a test dataset and 0.72 in all other cancer types together. Our method showed excellent performance for identifying cancer-specific mutations with an average sensitivity, specificity, and accuracy of 96.56 %, 97.39 %, and 97.64 %, respectively. We developed a web server for identifying cancer-prone sites, and it is available at https://web.iitm.ac.in/bioinfo2/MutBLESS/index.html. We suggest that our method can serve as an effective method to identify disease-prone sites and assist to develop therapeutic strategies.