Tumor area and microscopic extent of invasion to determine circulating tumor DNA fraction in plasma and detectability of colorectal cancer (CRC).

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 243-243
Author(s):  
Joerg Bredno ◽  
Jafi Lipson ◽  
Oliver Venn ◽  
Samuel Gross ◽  
Alexander P. Fields ◽  
...  
Keyword(s):  
Surface Area ◽  
Tumor Size ◽  
Clinical Stage ◽  
Circulating Tumor Dna ◽  
Linear Increase ◽  
Model Parameters ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Genome Bisulfite Sequencing ◽  
Cancer Types

243 Background: Circulating Cell-free Genome Atlas (CCGA; NCT02889978) is a multi-center, case-control, observational study with longitudinal follow-up to develop a cfDNA assay in which classifiers were trained on whole-genome bisulfite sequencing (WGBS) and targeted methylation (TM) sequencing data for detection of multiple cancer types. Previously, we showed that the fraction of ctDNA fragments (TF) was a stronger predictor of cancer detection than clinical stage and an equivalent predictor for survival. Given that CRC tumors can be described via surface area (TSA) and microscopic tumor extent (microinvasion), CRC was used as a model to examine the biophysical determinants of TF. Methods: Detection of multiple cancers with WGBS at 98% and TM at > 99% specificity, and methods for determining TF, were previously reported. A model to predict the presence of detectable cfDNA fragments for CRC adenocarcinomas of stages I, II, and III included TSA and microinvasion beyond the subserosa. Predictors were combined assuming a linear increase of cfDNA shedding with tumor size, with scaling factors depending on microinvasion. Model parameters were determined for 27 participants (7, 11, 9 for stages I, II, III, resp.) with WGBS and applied to 40 participants (12, 15, 13 for I, II, III, resp.) with TM assay and information on tumor size and microinvasion. Results: CRC detection at stages I/II/III was 33/46, 61/73, 57/74% for WGBS/TM. TF predicted detection with AUC = 97.6. The model predicted TF as TSA multiplied by 3.81*10−6 / mm2 for tumors that invaded beyond the subserosa (p < 0.001). This was 4.4x higher than estimates for tumors below the subserosa. The model trained on the WGBS assay predicted CRC detection in the TM assay with an AUC of 0.844. Conclusions: This model used TSA (number of tumor cells) and microinvasion (bloodstream access) to predict the fraction of CRC ctDNA fragments in blood without needing to account for stage. Tumors not penetrating the subserosa had low ctDNA shedding that likely limited detection. These findings may generalize to other cancer types, providing principles to predict ctDNA shedding and thus cancer detectability based on microinvasion and surface area. Clinical trial information: NCT02889978.

Predicting overall survival (OS) and overall response (OR) following durvalumab treatment in patients with multiple cancer types using a hybrid modeling strategy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15161-e15161
Author(s):  
Ting Chen ◽  
Yanan Zheng ◽  
Lorin Roskos ◽  
Donald E Mager
Keyword(s):  
Kinetic Model ◽  
Kinetic Parameters ◽  
Rate Constant ◽  
Tumor Size ◽  
Hybrid Modeling ◽  
Tumor Growth Rate ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Cancer Types ◽  
Modeling Strategy

e15161 Background: This study aimed to predict OS/OR and identify key predictors in patients with diverse cancer types treated with durvalumab, a PD-L1 targeting monoclonal antibody, using a hybrid modeling strategy that combines population pharmacodynamic (PD) modeling and machine learning (ML) algorithms. Methods: Individual longitudinal tumor size measurements and OS/OR data were available for 855 patients who received durvalumab therapy (10 mg/kg Q2W or 20 mg/kg Q4W; NCT01693562). Nine cancer types included non-small cell lung cancer (NSCLC), bladder cancer (BC), microsatellite instability-high (MSI-H) cancer, hepatocellular carcinoma (HCC), squamous cell carcinoma of the head and neck (SCCHN), gastroesophageal cancer (GEC), ovarian cancer (OC), pancreatic adenocarcinoma (PDAC) and triple-negative breast cancer (TNBC). A tumor kinetic model was developed to characterize diverse temporal profiles using a population-based modeling approach. Individual estimated tumor kinetic model parameters and patient demographic/physiological factors were used as inputs for predicting OS/OR using several ML approaches. Results: The final tumor kinetic model with liver metastasis (LM), neutrophil/lymphocyte ratio (NLR), tumor size at baseline (TBSL) and cancer types as covariates characterized the temporal tumor size data well. HCC and MSI-H cancer have the slowest tumor growth rate constant (kg), while GEC, SCCHN and TNBC have the fastest kg. BC, NSCLC and OC have the highest tumor killing rate constant. The most important predictors of OS identified by ML approach were tumor kinetic parameters (kg, fraction of drug-sensitive cells, time-delay in immune response), along with baseline disease factors, including hemoglobin (HGBBL), albumin (ALB), and NLR. Decision tree-based algorithms showed the best performance in predicting OR with accuracy above 90%. In addition to tumor kinetic parameters, PD-L1 expression on tumor cells (TC) and ALB were the most important predictors of OR. Conclusions: A combined population PD/ML approach showed good predictions of OS/OR in patients with different cancer types treated with durvalumab. LM, NLR,TBSL and cancer types were found to be important factors for tumor kinetics. In addition to tumor kinetic parameters, HGBBL, ALB, and NLR were found to be important predictors of OS, and TC and ALB were found to be important predictors of OR. These findings could provide a guidance on patient selection in future clinical trials.

scholarly journals Individualized ctDNA Fingerprints to Monitor Treatment Response and Recurrence in Multiple Cancer Types

2019 ◽  
Author(s):  
Jiaping Li ◽  
Wei Jiang ◽  
Jinwang Wei ◽  
Jianwei Zhang ◽  
Linbo Cai ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Treatment Response ◽  
Whole Exome Sequencing ◽  
Therapeutic Response ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Exome Sequencing Data ◽  
Whole Exome ◽  
Whole Exome Sequencing Data ◽  
Cancer Types

AbstractCirculating tumor DNA (ctDNA) panels hold high promise of accurately predicting the therapeutic response of tumors while being minimally invasive and cost-efficient. However, their use has been limited to a small number of tumor types and patients. Here, we developed individualized ctDNA fingerprints suitable for most patients with multiple cancer types. The panels were designed based on individual whole-exome sequencing data in 521 Chinese patients and targeting high clonal population clusters of somatic mutations. Together, these patients represent 12 types of cancers and seven different treatments. The customized ctDNA panels have a median somatic mutation number of 19, most of which are patient-specific rather than cancer hotspot mutations; 66.8% of the patients were ctDNA-positive. We further evaluated the ctDNA content fraction (CCF) of the mutations, and analyzed the association between the change of ctDNA concentration and therapeutic response. We followed up 106 patients for clinical evaluation, demonstrating a significant correlation of changes in ctDNA with clinical outcomes, with a consistency rate of 93.4%. In particular, the median CCF increased by 204.6% in patients with progressive disease, decreased by 82.5% in patients with remission, and was relatively stable in patients with stable disease. Overall, 85% of the patients with a ctDNA-positive status experienced metastasis or relapse long before imaging detection, except for two patients who developed recurrence and metastasis almost simultaneously. The average lead time between the first ctDNA-positive finding and radiological diagnosis was 76 days in three patients that changed from a ctDNA-negative to -positive status. Our individualized ctDNA analysis can effectively monitor the treatment response, metastasis, and recurrence in multiple cancer types in patients with multiple treatment options, therefore offering great clinical applicability for improving personalized treatment in cancer.One Sentence SummaryctDNA fingerprint panels were customized to predict the treatment response for multiple cancer types from individual whole-exome sequencing data.

Tumor-microbiome links subtype, cellular programs, and immunity in pancreatic cancer

2021 ◽  
Author(s):  
Martin Blaser ◽  
Bassel Ghaddar ◽  
Antara Biswas ◽  
Chris Harris ◽  
M. Bishr Omary ◽  
...  
Keyword(s):  
Tumor Infiltrating Lymphocytes ◽  
Cell Types ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Clinical Prognosis ◽  
Tumor Subtypes ◽  
Cancer Types ◽  
Specific Tissue ◽  
Bacteria And Fungi ◽  
Infiltrating Lymphocytes

Abstract Microorganisms are detected in multiple cancer types, including in putatively sterile organs, but it is unclear whether this relates to specific tissue contexts and influences oncogenesis or anti-tumor responses in humans. We developed SAHMI, a framework to analyze host-microbiome interactions using single-cell sequencing data. Interrogating pancreatic ductal adenocarcinomas (PDA), we identified an altered and diverse tumor microbiome that includes known and novel tumor-associated bacteria and fungi. Specific somatic cell-types were enriched with particular microbes whose abundances correlated with select host gene expression and cancer hallmark activities. Nearly all tumor-infiltrating lymphocytes had infection-reactive transcriptional profiles. Pseudotime analysis provided evidence for tumor-microbial co-evolution and identified three tumor subtypes with distinct microbial, molecular, and clinical characteristics. Finally, using multiple independent datasets, a signature of increased intra-tumoral microbial diversity predicted clinical prognosis. Collectively, tumor-microbiome cross-talk appears to modulate tumorigenesis with implications for clinical management.

scholarly journals Contextual Classifications of Cancer Driver Genes

2019 ◽  
Author(s):  
Pramod Chandrashekar ◽  
Navid Ahmadinejad ◽  
Junwen Wang ◽  
Aleksandar Sekulic ◽  
Jan B. Egan ◽  
...  
Keyword(s):  
Computational Method ◽  
Cancer Type ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Driver ◽  
Link Type ◽  
Mutational Hotspots ◽  
Cancer Types ◽  
Cancer Driver Genes

ABSTRACTFunctions of cancer driver genes depend on cellular contexts that vary substantially across tissues and organs. Distinguishing oncogenes (OGs) and tumor suppressor genes (TSGs) for each cancer type is critical to identifying clinically actionable targets. However, current resources for context-aware classifications of cancer drivers are limited. In this study, we show that the direction and magnitude of somatic selection of missense and truncating mutations of a gene are suggestive of its contextual activities. By integrating these features with ratiometric and conservation measures, we developed a computational method to categorize OGs and TSGs using exome sequencing data. This new method, named genes under selection in tumors (GUST) shows an overall accuracy of 0.94 when tested on manually curated benchmarks. Application of GUST to 10,172 tumor exomes of 33 cancer types identified 98 OGs and 179 TSGs, >70% of which promote tumorigenesis in only one cancer type. In broad-spectrum drivers shared across multiple cancer types, we found heterogeneous mutational hotspots modifying distinct functional domains, implicating the synchrony of convergent and divergent disease mechanisms. We further discovered two novel OGs and 28 novel TSGs with high confidence. The GUST program is available at https://github.com/liliulab/gust. A database with pre-computed classifications is available at https://liliulab.shinyapps.io/gust

scholarly journals 92P First-results of the CLIMB360 study, a prospective molecular screening program across multiple cancer types based on circulating tumor DNA (ctDNA)

2021 ◽  
Vol 32 ◽  
pp. S396-S397
Author(s):  
J. Garcia-Corbacho ◽  
I. Victoria Ruiz ◽  
L. Angelats ◽  
A. Indacochea ◽  
J. Martínez-Vidal ◽  
...  
Keyword(s):  
Screening Program ◽  
Circulating Tumor Dna ◽  
Molecular Screening ◽  
Multiple Cancer ◽  
First Results ◽  
Cancer Types ◽  
Tumor Dna

scholarly journals SuperFreq: Integrated mutation detection and clonal tracking in cancer

2018 ◽  
Author(s):  
Christoffer Flensburg ◽  
Tobias Sargeant ◽  
Alicia Oshlack ◽  
Ian Majewski
Keyword(s):  
Clonal Evolution ◽  
The Cancer Genome Atlas ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Multiple Cancer ◽  
Cancer Genome Atlas ◽  
Scope Of Application ◽  
Cancer Types ◽  
Multiple Samples

AbstractAnalysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples.SuperFreq is implemented in R and available on github at https://github.com/ChristofferFlensburg/SuperFreq.

Anticancer Immunotoxins, Challenges, and Approaches

2020 ◽  
Vol 26 ◽  
Author(s):  
Maryam Dashtiahangar ◽  
Leila Rahbarnia ◽  
Safar Farajnia ◽  
Arash Salmaninejad ◽  
Arezoo Gowhari Shabgah ◽  
...  
Keyword(s):  
Therapeutic Strategy ◽  
Antibody Fragment ◽  
Clinical Use ◽  
Multiple Cancer ◽  
Main Obstacle ◽  
Cancer Types ◽  
Recombinant Immunotoxins ◽  
Cancerous Cells ◽  
High Immunogenicity ◽  
Novel Therapeutic

: The development of recombinant immunotoxins (RITs) as a novel therapeutic strategy has made a revolution in the treatment of cancer. RITs are resulting from the fusion of antibodies to toxin proteins for targeting and eliminating cancerous cells by inhibiting protein synthesis. Despite indisputable outcomes of RITs regarding inhibiting multiple cancer types, high immunogenicity has been known as the main obstacle in the clinical use of RITs. Various strategies have been proposed to overcome these limitations, including immunosuppressive therapy, humanization of the antibody fragment moiety, generation of immunotoxins originated from endogenous human cytotoxic enzymes, and modification of the toxin moiety to escape the immune system. This paper devoted to reviewing recent advances in the design of immunotoxins with lower immunogenicity.

scholarly journals Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Leah L. Weber ◽  
Mohammed El-Kebir
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Evolutionary Process ◽  
Treatment Decision ◽  
Real Data ◽  
Current Data ◽  
Fast Method ◽  
Sequencing Data ◽  
Evolutionary Trajectory ◽  
Cancer Types

Abstract Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

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