Biased Influences of Low Tumor Purity on Mutation Detection in Cancer

Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis

Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously. The framework employs autoencoders to learn the non-linear representation of the data and applies tensor analysis for feature learning. Further, the clustering method is used to stratify the patients into multiple cancer risk groups. Several omics were included in the experiments, namely methylation, somatic copy-number variation (SCNV), micro RNA (miRNA) and RNA sequencing (RNAseq) from two cancer types, including Glioma and Breast Invasive Carcinoma from the TCGA dataset. The results of this study are promising, as evidenced by the survival analysis and classification models, which outperformed the state-of-the-art. The patients can be significantly (p-value<0.05) divided into risk groups using extracted latent variables from the fused multi-omics data. The pipeline is open source to help researchers and clinicians identify the patients' risk groups using genomics.

Concordance in Oncogenic Alterations Between the Primary Tumor and Advanced Disease: Insights Into the Heterogeneity of Intrahepatic Cholangiocarcinoma

PURPOSE

Intrahepatic cholangiocarcinoma (ICCA) is characterized by significant phenotypic and clinical heterogeneities and poor response to systemic therapy, potentially related to underlying heterogeneity in oncogenic alterations. We aimed to characterize the genomic heterogeneity between primary tumors and advanced disease in patients with ICCA.

METHODS

Biopsy-proven CCA specimens (primary tumor and paired advanced disease [metastatic disease, progressive disease on systemic therapy, or postoperative recurrence]) from two institutions were subjected to targeted next-generation sequencing. Overall concordance (oncogenic driver mutations, copy number alterations, and fusion events) and mutational concordance (only oncogenic mutations) were compared across paired samples. A subgroup analysis was performed on the basis of exposure to systemic therapy. Patients with extrahepatic CCA (ECCA) were included as a comparison group.

RESULTS

Sample pairs from 65 patients with ICCA (n = 54) and ECCA (n = 11) were analyzed. The median time between sample collection was 19.6 months (range, 2.7-122.9). For the entire cohort, the overall oncogenic concordance was 49% and the mutational concordance was 62% between primary and advanced disease samples. Subgroup analyses of ICCA and ECCA revealed overall/mutational concordance rates of 47%/58% and 60%/84%, respectively. Oncogenic concordance was similarly low for pairs exposed to systemic therapy between sample collections (n = 50, 53% overall, 68% mutational). In patients treated with targeted therapy for IDH1/2 alterations (n = 6) or FGFR2 fusions (n = 3), there was 100% concordance between the primary and advanced disease specimens. In two patients, FGFR2 (n = 1) and IDH1 (n = 1) alterations were detected de novo in the advanced disease specimens.

CONCLUSION

The results reflect a high degree of heterogeneity in ICCA and argue for reassessment of the dominant driver mutations with change in disease status.

[Chinese Expert Consensus on the Clinical Practice of Non-small Cell Lung Cancer Fusion Gene Detection Based on RNA-based NGS]

RNA-based next-generation sequencing (NGS) has been recommended as a method for detecting fusion genes in non-small cell lung cancer (NSCLC) according to clinical practice guidelines and expert consensus. The primary targetable alterations in NSCLC consist of gene mutations and fusions, making the detection of gene mutations and fusions indispensable for assessing the feasibility of targeted therapies. Currently, the integration of DNA-based NGS and RNA-based NGS allows for simultaneous detection of gene mutations and fusions and has been partially implemented in clinical practice. However, standardized guidelines and criteria for the significance, application scenarios, and quality control of RNA-based NGS in fusion gene detection are still lacking in China. This consensus aims to provide further clarity on the practical significance, application scenarios, and quality control measures of RNA-based NGS in fusion gene detection. Additionally, it offers guiding recommendations to facilitate the clinical implementation of RNA-based NGS in the diagnosis and treatment of NSCLC, ultimately maximizing the benefits for patients from fusion gene detection.
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Molecular profile of poorly cohesive gastric carcinoma with special reference to survival

BACKGROUND

Patients with poorly cohesive gastric carcinoma (PCC) are known to have poor survival. However, detailed molecular biology of PCC has not been elucidated, except for mutations in CDH1 and RHOA. Additionally, the molecular profiles of signet-ring cell carcinoma (SRC) have not been fully investigated. We aimed to investigate the association between molecular profiles and survival in PCC and PCC subtypes.

METHODS

The present study included 455 patients with gastric adenocarcinoma underwent radical gastrectomy. Whole-exome sequencing and gene expression profiling were conducted. Patients were classified according to the WHO classification as PCC or non-PCC, with PCC being further classified into SRC, combined, and PCC not-otherwise-specified (NOS). Clinicopathological factors and survival were compared with molecular profiles.

RESULTS

Of the patients, 159 were classified with PCC, while 296 were classified with non-PCC. Among PCC, 44 were classified with SRC, 64 with combined, and 51 with PCC-NOS. Mutations in CDH1 and RHOA were remarkably more frequent in PCC than in non-PCC. PCC had worse overall survival (OS) and disease-specific survival (DSS) compared to non-PCC. For PCC, the SRC group had good OS and DSS, whereas PCC-NOS classification with CDH1 mutations was associated with extremely poor survival. In the PCC-NOS and combined groups, patients with mutations in the extracellular domain 1 of CDH1 had poor survival.

CONCLUSIONS

Our findings suggest that PCC has poorer survival than non-PCC. Accumulation of CDH1 and RHOA mutations are unique profiles in PCC. Among PCC, CDH1 mutations may play a crucial role in the survival of non-SRC PCC.

Bias and inconsistency in the estimation of tumour mutation burden

BACKGROUND

Tumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB.

METHODS

We used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth.

RESULTS

We found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity.

CONCLUSION

Our results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.

ETV1 Positively Correlated With Immune Infiltration and Poor Clinical Prognosis in Colorectal Cancer

Objective

Numerous studies recently suggested that the immune microenvironment could influence the development of colorectal cancer (CRC). These findings implied that the infiltration of immune cells could be a promising prognostic biomarker for CRC.

Methods

Furthermore, the Oncomine database and R2 platform analysis were applied in our research to validate CRC clinical prognosis via expression levels of polyoma enhancer activator 3 (PEA3) members. We explored the correlation of ETV1, ETV4, and ETV5 with tumor-infiltrating immune cells (TIICs) in CRC tumor microenvironments via the Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA). Immunohistochemistry (IHC) was used to validate our CRC clinical data.

Results

Our findings indicated that the upregulation of PEA3 members including ETV1 and ETV5 was positively associated with poor prognosis in CRC patients. Meanwhile, ETV1 and ETV5 may play significant roles in the development progress of CRC. Furthermore, ETV1 tends to be associated with immune infiltration of CRC, especially with cancer-associated fibroblasts and M2 macrophages.

Conclusion

These findings revealed that ETV1 and ETV5 played significant roles in the development of CRC. Moreover, ETV1 was significantly associated with the infiltration of cancer-associated fibroblasts and M2 macrophages in CRC. Targeting ETV1 can be a potential auspicious approach for CRC treatment.

RNA-SSNV: A Reliable Somatic Single Nucleotide Variant Identification Framework for Bulk RNA-Seq Data

The usage of expressed somatic mutations may have a unique advantage in identifying active cancer driver mutations. However, accurately calling mutations from RNA-seq data is difficult due to confounding factors such as RNA-editing, reverse transcription, and gap alignment. In the present study, we proposed a framework (named RNA-SSNV, https://github.com/pmglab/RNA-SSNV) to call somatic single nucleotide variants (SSNV) from tumor bulk RNA-seq data. Based on a comprehensive multi-filtering strategy and a machine-learning classification model trained with comprehensively curated features, RNA-SSNV achieved the best precision–recall rate (0.880–0.884) in a testing dataset and robustly retained 0.94 AUC for the precision–recall curve in three validation adult-based TCGA (The Cancer Genome Atlas) datasets. We further showed that the somatic mutations called by RNA-SSNV tended to have a higher functional impact and therapeutic power in known driver genes. Furthermore, VAF (variant allele fraction) analysis revealed that subclonal harboring expressed mutations had evolutional selection advantage and RNA had higher detection power to rescue DNA-omitted mutations. In sum, RNA-SSNV will be a useful approach to accurately call expressed somatic mutations for a more insightful analysis of cancer drive genes and carcinogenic mechanisms.

Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study

Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment.

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MIL model successfully predicts a sample's tumor purity from histopathology slides

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MIL model learns to spatially resolve tumor purity from sample-level labels

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Tumor purity varies spatially within a sample

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Pathologists’ region selection is vital for correct percentage tumor nuclei estimation

Given some big data and coarse-level labels, extracting fine-level information is a demanding yet rewarding challenge in data science. This study develops a machine learning model utilizing big data and exploiting coarse-level labels to reveal fine-level details within the data. Although it can be applied to different data science tasks with enormous data and coarse labels, we applied it to a computational histopathology task with gigapixel histopathology slides and sample-level labels. Specifically, the model revealed spatial resolution of tumor purity within histopathology slides using only sample-level genomic tumor purity values during training. This can also be extended to other omics features, providing precious information about cancer biology and promising personalized, precision medicine. Such studies are of great clinical importance in discovering imaging biomarkers and better understanding the tumor microenvironment.