An invasion front gene expression signature for higher-risk patient selection in stage IIA MSS colon cancer.

Stage II colon cancer (CC) encompasses a heterogeneous group of patients with diverse survival experiences: 87% to 58% 5-year relative survival rates for stages IIA and IIC, respectively. While stage IIA patients are usually spared the adjuvant chemotherapy, some of them relapse and may benefit from it; thus, their timely identification is crucial. Current gene expression signatures did not specifically target this group nor did they find their place in clinical practice. Since processes at invasion front have also been linked to tumor progression, we hypothesize that aside from bulk tumor features, focusing on the invasion front may provide additional clues for this stratification. A retrospective matched case-control collection of 39 stage IIA microsatellite-stable (MSS) untreated CCs was analyzed to identify prognostic gene expression-based signatures. The endpoint was defined as relapse within 5 years vs. no relapse for at least 6 years. From the same tumors, three different classifiers (bulk tumor, invasion front, and constrained baseline on bulk tumor) were developed and their performance estimated. The baseline classifier, while the weakest, was validated in two independent data sets. The best performing signature was based on invasion front profiles [area under the receiver operating curve (AUC) = 0.931 (0.815-1.0)] and contained genes associated with KRAS pathway activation, apical junction complex, and heme metabolism. Its combination with bulk tumor classifier further improved the accuracy of the predictions.

Molecular portraits of colorectal cancer morphological regions.

Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that whole-tumor molecular descriptors depend on the morphological heterogeneity with significant impact on current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology represented by six morphological patterns (morphotypes): complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptome profiling by microarrays of 202 tumor regions (morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF-α signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results show that morphotype-based tumor sampling allows the detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.

Functional In Vivo Screening Identifies microRNAs Regulating Metastatic Dissemination of Prostate Cancer Cells to Bone Marrow.

Distant metastasis is the major cause of cancer-related deaths in men with prostate cancer (PCa). An in vivo functional screen was used to identify microRNAs (miRNAs) regulating metastatic dissemination of PCa cells. PC3 cells transduced with pooled miRZiP™ lentivirus library (anti-miRNAs) were injected intraprostatic to 13 NSG mice followed by targeted barcode/anti-miR sequencing. PCa cells in the primary tumours showed a homogenous pattern of anti-miRNAs, but different anti-miRNAs were enriched in liver, lung, and bone marrow, with anti-miR-379 highly enriched in the latter. The bone metastasis-promoting phenotype induced by decreased miR-379 levels was also confirmed in a less metastatic PCa cell line, 22Rv1, where all mice injected intracardially with anti-miR-379-22Rv1 cells developed bone metastases. The levels of miR-379 were found to be lower in bone metastases compared to primary tumours and non-cancerous prostatic tissue in a patient cohort. In vitro functional studies suggested that the mechanism of action was that reduced levels of miR-379 gave an increased colony formation capacity in conditions mimicking the bone microenvironment. In conclusion, our data suggest that specific miRNAs affect the establishment of primary tumours and metastatic dissemination, with a loss of miR-379 promoting metastases in bone.

The Long Noncoding RNA CCAT2 Induces Chromosomal Instability Through BOP1-AURKB Signaling.

BACKGROUND & AIMS: Chromosomal instability (CIN) is a carcinogenesis event that promotes metastasis and resistance to therapy by unclear mechanisms. Expression of the colon cancer-associated transcript 2 gene (CCAT2), which encodes a long noncoding RNA (lncRNA), associates with CIN, but little is known about how CCAT2 lncRNA regulates this cancer enabling characteristic. METHODS: We performed cytogenetic analysis of colorectal cancer (CRC) cell lines (HCT116, KM12C/SM, and HT29) overexpressing CCAT2 and colon organoids from C57BL/6N mice with the CCAT2 transgene and without (controls). CRC cells were also analyzed by immunofluorescence microscopy, γ-H2AX, and senescence assays. CCAT2 transgene and control mice were given azoxymethane and dextran sulfate sodium to induce colon tumors. We performed gene expression array and mass spectrometry to detect downstream targets of CCAT2 lncRNA. We characterized interactions between CCAT2 with downstream proteins using MS2 pull-down, RNA immunoprecipitation, and selective 2'-hydroxyl acylation analyzed by primer extension analyses. Downstream proteins were overexpressed in CRC cells and analyzed for CIN. Gene expression levels were measured in CRC and non-tumor tissues from 5 cohorts, comprising more than 900 patients. RESULTS: High expression of CCAT2 induced CIN in CRC cell lines and increased resistance to 5-fluorouracil and oxaliplatin. Mice that expressed the CCAT2 transgene developed chromosome abnormalities, and colon organoids derived from crypt cells of these mice had a higher percentage of chromosome abnormalities compared with organoids from control mice. The transgenic mice given azoxymethane and dextran sulfate sodium developed more and larger colon polyps than control mice given these agents. Microarray analysis and mass spectrometry indicated that expression of CCAT2 increased expression of genes involved in ribosome biogenesis and protein synthesis. CCAT2 lncRNA interacted directly with and stabilized BOP1 ribosomal biogenesis factor (BOP1). CCAT2 also increased expression of MYC, which activated expression of BOP1. Overexpression of BOP1 in CRC cell lines resulted in chromosomal missegregation errors, and increased colony formation, and invasiveness, whereas BOP1 knockdown reduced viability. BOP1 promoted CIN by increasing the active form of aurora kinase B, which regulates chromosomal segregation. BOP1 was overexpressed in polyp tissues from CCAT2 transgenic mice compared with healthy tissue. CCAT2 lncRNA and BOP1 mRNA or protein were all increased in microsatellite stable tumors (characterized by CIN), but not in tumors with microsatellite instability compared with nontumor tissues. Increased levels of CCAT2 lncRNA and BOP1 mRNA correlated with each other and with shorter survival times of patients. CONCLUSIONS: We found that overexpression of CCAT2 in colon cells promotes CIN and carcinogenesis by stabilizing and inducing expression of BOP1 an activator of aurora kinase B. Strategies to target this pathway might be developed for treatment of patients with microsatellite stable colorectal tumors.

Assessment of Tumor Mutational Burden in Pediatric Tumors by Real-Life Whole-Exome Sequencing and In Silico Simulation of Targeted Gene Panels: How the Choice of Method Could Affect the Clinical Decision?

BACKGROUND: Tumor mutational burden (TMB) is an emerging genomic biomarker in cancer that has been associated with improved response to immune checkpoint inhibitors (ICIs) in adult cancers. It was described that variability in TMB assessment is introduced by different laboratory techniques and various settings of bioinformatic pipelines. In pediatric oncology, no study has been published describing this variability so far. METHODS: In our study, we performed whole exome sequencing (WES, both germline and somatic) and calculated TMB in 106 patients with high-risk/recurrent pediatric solid tumors of 28 distinct cancer types. Subsequently, we used WES data for TMB calculation using an in silico approach simulating two The Food and Drug Administration (FDA)-approved/authorized comprehensive genomic panels for cancer. RESULTS: We describe a strong correlation between WES-based and panel-based TMBs; however, we show that this high correlation is significantly affected by inclusion of only a few hypermutated cases. In the series of nine cases, we determined TMB in two sequentially collected tumor tissue specimens and observed an increase in TMB along with tumor progression. Furthermore, we evaluated the extent to which potential ICI indication could be affected by variability in techniques and bioinformatic pipelines used for TMB assessment. We confirmed that this technological variability could significantly affect ICI indication in pediatric cancer patients; however, this significance decreases with the increasing cut-off values. CONCLUSIONS: For the first time in pediatric oncology, we assessed the reliability of TMB estimation across multiple pediatric cancer types using real-life WES and in silico analysis of two major targeted gene panels and confirmed a significant technological variability to be introduced by different laboratory techniques and various settings of bioinformatic pipelines.