SB Digestor: a tailored driver gene identification tool for dissecting heterogeneous Sleeping Beauty transposon-induced tumors.

Sleeping Beauty (SB) insertional mutagenesis has been widely used for genome-wide functional screening in mouse models of human cancers, however, intertumor heterogeneity can be a major obstacle in identifying common insertion sites (CISs). Although previous algorithms have been successful in defining some CISs, they also miss CISs in certain situations. A major common characteristic of these previous methods is that they do not take tumor heterogeneity into account. However, intertumoral heterogeneity directly influences the sequence read number for different tumor samples and then affects CIS identification. To precisely detect and define cancer driver genes, we developed SB Digestor, a computational algorithm that overcomes biological heterogeneity to identify more potential driver genes. Specifically, we define the relationship between the sequenced read number and putative gene number to deduce the depth cutoff for each tumor, which can reduce tumor complexity and precisely reflect intertumoral heterogeneity. Using this new tool, we re-analyzed our previously published SB-based screening dataset and identified many additional potent drivers involved in Brca1-related tumorigenesis, including Arhgap42, Tcf12, and Fgfr2. SB Digestor not only greatly enhances our ability to identify and prioritize cancer drivers from SB tumors but also substantially deepens our understanding of the intrinsic genetic basis of cancer.

Immunogenomic intertumor heterogeneity across primary and metastatic sites in a patient with lung adenocarcinoma.

BACKGROUND: Lung cancer is the leading cause of cancer death, partially owing to its extensive heterogeneity. The analysis of intertumor heterogeneity has been limited by an inability to concurrently obtain tissue from synchronous metastases unaltered by multiple prior lines of therapy. METHODS: In order to study the relationship between genomic, epigenomic and T cell repertoire heterogeneity in a rare autopsy case from a 32-year-old female never-smoker with left lung primary late-stage lung adenocarcinoma (LUAD), we did whole-exome sequencing (WES), DNA methylation and T cell receptor (TCR) sequencing to characterize the immunogenomic landscape of one primary and 19 synchronous metastatic tumors. RESULTS: We observed heterogeneous mutation, methylation, and T cell patterns across distinct metastases. Only TP53 mutation was detected in all tumors suggesting an early event while other cancer gene mutations were later events which may have followed subclonal diversification. A set of prevalent T cell clonotypes were completely excluded from left-side thoracic tumors indicating distinct T cell repertoire profiles between left-side and non left-side thoracic tumors. Though a limited number of predicted neoantigens were shared, these were associated with homology of the T cell repertoire across metastases. Lastly, ratio of methylated neoantigen coding mutations was negatively associated with T-cell density, richness and clonality, suggesting neoantigen methylation may partially drive immunosuppression. CONCLUSIONS: Our study demonstrates heterogeneous genomic and T cell profiles across synchronous metastases and how restriction of unique T cell clonotypes within an individual may differentially shape the genomic and epigenomic landscapes of synchronous lung metastases.

Intertumor and intratumor heterogeneity of PIK3CA mutations in extramammary Paget's disease.

Although the prognosis of patients with extramammary Paget's disease (EMPD) treated with radical resection is good, the prognosis of EMPD with distant metastasis is very poor. PIK3CA mutations predict a good response to PIK3CA inhibitors. The aim of this study was to investigate the occurrence rate of PIK3CA mutations (including multiple mutations [MM]) related to the intertumor and intratumor heterogeneity in EMPD and to evaluate the correlation between these mutations and clinical parameters of EMPD. We performed droplet digital polymerase chain reaction to detect PIK3CA mutations (E542K, E545K, H1047R, and MM) in 68 patients with EMPD. In addition, we investigated the presence of PIK3CA mutations at multiple sites in 16 patients with PIK3CA mutations to assess the intratumor heterogeneity of PIK3CA mutations in EMPD. The frequency of one or more PIK3CA mutations in patients with EMPD was 30.8% (21/68). The frequency of E542K, E545K, H1047R, and MM were 10.2% (7/68), 13.2% (9/68), 11.7% (8/68), and 4.4% (3/68), respectively. No significant correlation was found between PIK3CA mutation patterns and clinical parameters. Of the 21 patients with PIK3CA mutations, 16 with tissue samples that could be analyzed at multiple sites were examined. The proportion of patients with the same PIK3CA mutations at all sites was 12.5% (2/16). The proportion of patients with the same PIK3CA mutations at least two or more sites, but not at all sites, was 31.2% (5/16). The proportion of patients with no PIK3CA mutations at other sites was 37.5% (6/16). The proportion of patients with other PIK3CA mutations at other sites was 18.7% (3/16). There is intertumor and intratumor heterogeneity of PIK3CA mutations. PIK3CA mutations in EMPD may be progressor mutations in EMPD.

SETDB1 Overexpression Sets an Intertumoral Transcriptomic Divergence in Non-small Cell Lung Carcinoma.

An increasing volume of evidence suggests that SETDB1 plays a role in the tumorigenesis of various cancers, classifying SETDB1 as an oncoprotein. However, owing to its numerous protein partners and their global-scale effects, the molecular mechanism underlying SETDB1-involved oncogenesis remains ambiguous. In this study, using public transcriptome data of lung adenocarcinoma (ADC) and squamous-cell carcinoma (SCC), we compared tumors with high-level SETDB1 (SH) and those with low-level SETDB1 (comparable with normal samples; SL). The results of principal component analysis revealed a transcriptomic distinction and divergence between the SH and SL samples in both ADCs and SCCs. The results of gene set enrichment analysis indicated that genes involved in the "epithelial-mesenchymal transition," "innate immune response," and "autoimmunity" collections were significantly depleted in SH tumors, whereas those involved in "RNA interference" collections were enriched. Chromatin-modifying genes were highly expressed in SH tumors, and the variance in their expression was incomparably high in SCC-SH, which suggested greater heterogeneity within SCC tumors. DNA methyltransferase genes were also overrepresented in SH samples, and most differentially methylated CpGs (SH/SL) were undermethylated in a highly biased manner in ADCs. We identified interesting molecular signatures associated with the possible roles of SETDB1 in lung cancer. We expect these SETDB1-associated molecular signatures to facilitate the development of biologically relevant targeted therapies for particular types of lung cancer.

Cooperation Between Distinct Cancer Driver Genes Underlies Intertumor Heterogeneity in Hepatocellular Carcinoma.

BACKGROUND AND AIMS: The pattern of genetic alterations in cancer driver genes in patients with hepatocellular carcinoma (HCC) is highly diverse, which partially explains the low efficacy of available therapies. In spite of this, the existing mouse models only recapitulate a small portion of HCC inter-tumor heterogeneity, limiting the understanding of the disease and the nomination of personalized therapies. Here, we aimed at establishing a novel collection of HCC mouse models that captured human HCC diversity. METHODS: By performing hydrodynamic tail-vein injections, we tested the impact of altering a well-established HCC oncogene (either MYC or ß-catenin) in combination with an additional alteration in one of eleven other genes frequently mutated in HCC. Of the 23 unique pairs of genetic alterations that we interrogated, 9 were able to induce HCC. The established HCC mouse models were characterized at histopathological, immune, and transcriptomic level to identify the unique features of each model. Murine HCC cell lines were generated from each tumor model, characterized transcriptionally, and used to identify specific therapies that were validated in vivo. RESULTS: Cooperation between pairs of driver genes produced HCCs with diverse histopathology, immune microenvironments, transcriptomes, and drug responses. Interestingly, MYC expression levels strongly influenced ß-catenin activity, indicating that inter-tumor heterogeneity emerges not only from specific combinations of genetic alterations but also from the acquisition of expression-dependent phenotypes. CONCLUSIONS: This novel collection of murine HCC models and corresponding cell lines establishes the role of driver genes in diverse contexts and enables mechanistic and translational studies.

Multiregion Quantification of Extracellular Signal-regulated Kinase Activity in Renal Cell Carcinoma.

To personalize treatment for renal cell carcinoma (RCC), it would be ideal to confirm the activity of druggable protein pathways within individual tumors. We have developed a high-resolution nanoimmunoassay (NIA) to measure protein activity with high precision in scant specimens (eg, fine needle aspirates [FNAs]). Here, we used NIA to determine whether protein activation varied in different regions of RCC tumors. Since most RCC therapies target angiogenesis by inhibiting the vascular endothelial growth factor (VEGF) receptor, we quantified phosphorylation of extracellular signal-regulated kinase (ERK), a downstream effector of the VEGF signaling pathway. In 90 ex vivo FNA biopsies sampled from multiple regions of 38 primary clear cell RCC tumors, ERK phosphorylation differed among patients. In contrast, within individual patients, we found limited intratumoral heterogeneity of ERK phosphorylation. Our results suggest that measuring ERK in a single FNA may be representative of ERK activity in different regions of the same tumor. As diagnostic and therapeutic protein biomarkers are being sought, NIA measurements of protein signaling may increase the clinical utility of renal mass biopsy and allow for the application of precision oncology for patients with localized and advanced RCC. PATIENT SUMMARY: In this report, we applied a new approach to measure the activity of extracellular signal-regulated kinase (ERK), a key cancer signaling protein, in different areas within kidney cancers. We found that ERK activity varied between patients, but that different regions within individual kidney tumors showed similar ERK activity. This suggests that a single biopsy of renal cell carcinoma may be sufficient to measure protein signaling activity to aid in precision oncology approaches.

Multifocal primary neuroblastoma tumor heterogeneity in siblings with co-occurring PHOX2B and NF1 genetic aberrations.

Neuroblastoma, the most common extracranial solid tumor of childhood, can present in multiple primary sites, but the extent of genetic heterogeneity among tumor foci, as well as the presence or absence of common oncogenic drivers, remains unknown. Although PHOX2B genetic aberrations can cause familial neuroblastoma, they demonstrate incomplete penetrance with respect to neuroblastoma pathogenesis, suggesting that additional undescribed oncogenic drivers are necessary for tumor development. We performed comprehensive molecular characterization of neuroblastoma tumors from two siblings affected by familial multifocal neuroblastoma, including whole exome sequencing and single-nucleotide polymorphism (SNP) arrays of tumor and matched blood samples. Data were processed and analyzed using established bioinformatics algorithms to evaluate for germline and somatic mutations and copy number variations (CNVs). We confirmed the presence of a PHOX2B deletion and NF1 mutation across all tumor samples and the germline genome. Matched tumor-blood whole exome sequencing also identified 365 genes that contained nonsilent coding mutations across all tumor samples, with no recurrent mutations across all tumors. SNP arrays also showed significant heterogeneity with respect to CNVs. The only common CNV across all tumors was 17q gain, with differing chromosomal coordinates across samples but a common region of overlap distal to 17q21.31, suggesting this adverse prognostic biomarker may offer insight about additional drivers for multifocal neuroblastoma in patients with germline PHOX2B or NF1 aberrations. Molecular characterization of all tumors from patients with multifocal primary neuroblastoma has potential to yield novel insights on neuroblastoma pathogenesis.

Intertumor heterogeneity in 60 pancreatic neuroendocrine tumors associated with multiple endocrine neoplasia type 1.

BACKGROUND: Patients with multiple endocrine neoplasia type 1 (MEN-1) develop multiple pancreatic neuroendocrine neoplasias (PNENs). Size at diagnosis and growth during follow-up are crucial parameters. According to the WHO 2017, grading is another important parameter. The impact of grading compared to size (WHO 2000) on the clinical course needs to be evaluated. METHODS: Sixty PNENs of six patients with MEN-1 were retrospectively evaluated. RESULTS: Fifty-one tumors with a diameter of < 20 mm were graded as G1. Two of 9 tumors with diameters of ≥20 mm were graded as G2. Tumor size of ≥20 mm correlated significantly with higher proliferation (p = 0.000617). Lymph node metastases were documented in two patients with a total of 19 tumors. In one patient, all 13 tumors (diameter: 0.4 to 100 mm) were classified as G1. However, metastases were documented in 9/29 lymph nodes. In the other patient, 5 tumors (3.5 to 20 mm) were classified as G1. The sixth tumor (30 mm) was classified as G2 (Ki-67: 8%). Metastases were revealed in 2/20 lymph nodes. CONCLUSIONS: Tumor size of ≥20 mm seems to correlate with more aggressive MEN-1 related pancreatic disease, regardless of individual proliferation. Tumors ≥20 mm and tumors graded as G2 should be treated surgically regardless of their size.

Clonality and heterogeneity of metachronous colorectal cancer.

Patients with metachronous colorectal cancer (CRC) have been diagnosed with primary CRC more than once. Given that the genetic and microenvironment is the same in these cases, metachronous CRC is an important model for studying colorectal tumorigenesis. We performed whole exome sequencing of seven freshly frozen tumors from three patients with metachronous CRC and compared their genetic profiles. In patients with metachronous tumors of distinct genetic origins, 3.74% and 0.20% of genes were ubiquitously mutated and candidate cancer genes mutated at different sites. Tumors from the same patients were clonally unrelated, and thus druggable genes differed. In contrast, in a patient with metachronous tumors of a common genetic origin, the ubiquitously mutated genes were 61.02%, with ubiquitously mutated genes and candidate cancer genes all mutated at the same sites, tumors were clonally related, and some druggable genes were the same. Therefore, two different clonal relationships between metachronous tumors exist in CRC, one is monoclonal and the other is polyclonal. Our findings may help to advance understanding of the differences in metachronous CRCs and the genetic mechanisms of which they originate, and provide new avenues for CRC treatment.

Personalized disease signatures through information-theoretic compaction of big cancer data.

Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study a number of datasets spanning five different cancer types, aiming to capture the extensive interpatient heterogeneity that exists within a specific cancer type as well as between cancers of different origins. We show that a relatively small number of altered molecular processes suffices to accurately characterize over 500 tumors, showing extreme compaction of the data. Every patient is characterized by a small specific subset of unbalanced processes. We validate the result by verifying that the processes identified characterize other cancer patients as well. We show that different patients may display similar oncogene expression levels, albeit carrying biologically distinct tumors that harbor different sets of unbalanced molecular processes. Thus, tumors may be inaccurately classified and addressed as similar. These findings highlight the need to expand the notion of tumor-specific oncogenic biomarkers to patient-specific, comprehensive transcriptional networks for improved patient-tailored diagnostics.

Whole exome sequencing reveals intertumor heterogeneity and distinct genetic origins of sporadic synchronous colorectal cancer.

Sporadic synchronous colorectal cancer (CRC) refers to more than one primary tumor detected in a single patient at the time of the first diagnosis without predisposition of cancer development. Given the same genetic and microenvironment they raise, sporadic synchronous CRC is a unique model to study CRC tumorigenesis. We performed whole exome sequencing in 32 fresh frozen tumor lesions from 15 patients with sporadic synchronous CRC to compare their genetic alterations. This approach identified ubiquitously mutated genes in the range from 0.34% to 4.22% and from 0.8% to 7.0% in non-hypermutated tumors and hypermutated tumors, respectively, in a single patient. We show that both ubiquitously mutated genes and candidate cancer genes from different tumors in the same patient mutated at different sites. Consistently, obvious differences in somatic copy number variations (SCNV) were found in most patients with non-hypermutated tumor lesions, which had ubiquitous copy number amplification rates ranging from 0% to 8.8% and ubiquitous copy number deletion rates ranging from 0% to 8.2%. Hypermutated lesions were nearly diploid with 0% to 18.8% common copy number aberrations. Accordingly, clonal structures, altered signaling pathways and druggable genes in a single patient with synchronous CRC varied significantly. Taken together, the disparate SCNVs and mutations in synchronous CRC supported the field effect theory of tumorigenesis. Moreover, the intertumor heterogeneity of synchronous CRCs implies that analysis of all tumor lesions from the same patient is necessary for appropriate clinical treatment decisions.

Current Progresses of Single Cell DNA Sequencing in Breast Cancer Research.

Breast cancers display striking genetic and phenotypic diversities. To date, several hypotheses are raised to explain and understand the heterogeneity, including theories for cancer stem cell (CSC) and clonal evolution. According to the CSC theory, the most tumorigenic cells, while maintaining themselves through symmetric division, divide asymmetrically to generate non-CSCs with less tumorigenic and metastatic potential, although they can also dedifferentiate back to CSCs. Clonal evolution theory recapitulates that a tumor initially arises from a single cell, which then undergoes clonal expansion to a population of cancer cells. During tumorigenesis and evolution process, cancer cells undergo different degrees of genetic instability and consequently obtain varied genetic aberrations. Yet the heterogeneity in breast cancers is very complex, poorly understood and subjected to further investigation. In recent years, single cell sequencing (SCS) technology developed rapidly, providing a powerful new way to better understand the heterogeneity, which may lay foundations to some new strategies for breast cancer therapies. In this review, we will summarize development of SCS technologies and recent advances of SCS in breast cancer.

A Direct Test of Selection in Cell Populations Using the Diversity in Gene Expression within Tumors.

Although intratumor diversity driven by selection has been the prevailing view in cancer biology, recent population genetic analyses have been unable to reject the neutral interpretation. As the power to reject neutrality in tumors is often low, it will be desirable to have an alternative means to test selection directly. Here, we utilize gene expression data as a surrogate for functional significance in intra- and intertumor comparisons. The expression divergence between samples known to be driven by selection (e.g., between tumor and normal tissues) is always higher than the divergence between normal samples, which should be close to the neutral level of divergence. In contrast, the expression differentiation between regions of the same tumor, being lower than the neutral divergence, is incompatible with the hypothesis of selectively driven divergence. To further test the hypothesis of neutral evolution, we select a hepatocellular carcinoma tumor that has large intratumor SNV and CNV (single nucleotide variation and copy number variation, respectively) diversity. This tumor enables us to calibrate the level of expression divergence against that of genetic divergence. We observe that intratumor divergence in gene expression profile lags far behind genetic divergence, indicating insufficient phenotypic differences for selection to operate. All these expression analyses corroborate that natural selection does not operate effectively within tumors, supporting recent interpretations of within-tumor diversity. As the expected level of genetic diversity, hence the potential for drug resistance, would be much higher under neutrality than under selection, the issue is of both theoretical and clinical significance.

Tumoral heterogeneity of breast cancer.

The objective of this literature review is to describe the types of tumor heterogeneity in breast cancer and their clinical implication. Two kinds of tumor heterogeneity are described: intertumor heterogeneity and intra-tumor heterogeneity. In breast cancer, inter-tumor heterogeneity was best characterized in the 2000s thanks to high throughput analyses. These analyzes resulted in a molecular classification of breast cancers distinguishing four subtypes: Luminal A, Luminal B, HER 2+ and basal like. This variability may be observed between the primary tumor and metastases, namely the temporal intratumor heterogeneity. The average discrepancy for the progesterone receptor, estrogen, and between HER2 status appears to be 33%, 20% and 8%, respectively. It is then interesting to study the heterogeneity within the primary tumor: this spatial intra-tumor heterogeneity is poorly known. Physiopathology of intra-tumor heterogeneity light be deciphered by studies on cancer stem cells and clonal evolution model. In addition, the tumor microenvironment seems to actively contribute to this heterogeneity. The major interest to study this heterogeneity is the clinical implication that could result. While precision medicine is emerging, it is important to capture the heterogeneity of each specific tumor type. These new biological knowledge will allow us to anticipate such heterogeneity and individualize the management of breast cancer.

New horizons for targeted treatment of neuroendocrine tumors.

Neuroendocrine tumors (NETs) are rare and heterogeneous tumors and there is a paucity of randomized clinical trials evaluating the different therapeutic strategies. Over recent years, some important molecular aspects have been investigated and multiple targeted therapies are currently available. One of the most promising targets for the therapy of NETs are the mTOR and angiogenic growth factor receptors. The advent of the inhibitors of the mTOR pathway, tyrosine kinase inhibitors and of somatostatin analogs have shown their efficacy in randomized clinical trials in terms of implementing clinical hormone-induced syndromes and progression-free survival of advanced NETs. This article summarizes the standard therapies and new perspectives in NET's treatment, which remains still very heterogeneous and little known entity.

Cancer Bioinformatic Methods to Infer Meaningful Data From Small-Size Cohorts.

Whole-genome analyses have uncovered that most cancer-relevant genes cluster into 12 signaling pathways. Knowledge of the signaling pathways and associated gene signatures not only allows us to understand the mechanisms of oncogenesis inherent to specific cancers but also provides us with drug targets, molecular diagnostic and prognosis factors, as well as biomarkers for patient risk stratification and treatment. Publicly available genomic data sets constitute a wealth of gene mining opportunities for hypothesis generation and testing. However, the increasingly recognized genetic and epigenetic inter- and intratumor heterogeneity, combined with the preponderance of small-size cohorts, hamper reliable analysis and discovery. Here, we review two methods that are used to infer meaningful biological events from small-size data sets and discuss some of their applications and limitations.