CIBRA identifies genomic alterations with a system-wide impact on tumor biology

Background: Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating genomic alterations (e.g., genomics), and another defining a system-wide expression response (e.g., transcriptomics). CIBRA was evaluated with genome-wide screens in 33 cancer types using primary and metastatic cancer data from the Cancer Genome Atlas and Hartwig Medical Foundation. Results: We demonstrate the capability of CIBRA by successfully confirming the impact of point mutations in experimentally validated oncogenes and tumor suppressor genes. Surprisingly, many genes affected by structural variants were identified to have a strong system-wide impact (30.3%), suggesting that their role in cancer development has thus far been largely underreported. Additionally, CIBRA can identify impact with only ten cases and controls, providing a novel way to prioritize genomic alterations with a prominent role in cancer biology. Conclusions: Our findings demonstrate that CIBRA can identify cancer drivers by combining genomics and transcriptomics data. Moreover, our work shows an unexpected substantial system-wide impact of structural variants in cancer. Hence, CIBRA has the potential to preselect and refine current definitions of genomic alterations to derive more nuanced biomarkers for diagnostics, disease progression, and treatment response. CIBRA is available at https://github.com/AIT4LIFE-UU/CIBR

Most Lung and Colon Cancer Susceptibility Genes Are Pair-Wise Linked in Mice, Humans and Rats

Genetic predisposition controlled by susceptibility quantitative trait loci (QTLs) contributes to a large proportion of common cancers. Studies of genetics of cancer susceptibility, however, did not address systematically the relationship between susceptibility to cancers in different organs. We present five sets of data on genetic architecture of colon and lung cancer susceptibility in mice, humans and rats. They collectively show that the majority of genes for colon and lung cancer susceptibility are linked pair-wise and are likely identical or related. Four CcS/Dem recombinant congenic strains, each differing from strain BALB/cHeA by a different small random subset of ±12.5% of genes received from strain STS/A, suggestively show either extreme susceptibility or extreme resistance for both colon and lung tumors, which is unlikely if the two tumors were controlled by independent susceptibility genes. Indeed, susceptibility to lung cancer (Sluc) loci underlying the extreme susceptibility or resistance of such CcS/Dem strains, mapped in 226 (CcS-10×CcS-19)F2 mice, co-localize with susceptibility to colon cancer (Scc) loci. Analysis of additional Sluc loci that were mapped in OcB/Dem strains and Scc loci in CcS/Dem strains, respectively, shows their widespread pair-wise co-localization (P = 0.0036). Finally, the majority of published human and rat colon cancer susceptibility genes map to chromosomal regions homologous to mouse Sluc loci. 12/12 mouse Scc loci, 9/11 human and 5/7 rat colon cancer susceptibility loci are close to a Sluc locus or its homologous site, forming 21 clusters of lung and colon cancer susceptibility genes from one, two or three species. Our data shows that cancer susceptibility QTLs can have much broader biological effects than presently appreciated. It also demonstrates the power of mouse genetics to predict human susceptibility genes. Comparison of molecular mechanisms of susceptibility genes that are organ-specific and those with trans-organ effects can provide a new dimension in understanding individual cancer susceptibility

Prognostic value of microvessel density in stage II and III colon cancer patients:a retrospective cohort study

Background Microvessel density (MVD), as a derived marker for angiogenesis, has been associated with poor outcome in several types of cancer. This study aimed to evaluate the prognostic value of MVD in stage II and III colon cancer and its relation to tumour-stroma-percentage (TSP) and expression of HIF1A and VEGFA. Methods Formalin-fixed paraffin-embedded (FFPE) colon cancer tissues were collected from 53 stage II and 54 (5-fluorouracil-treated) stage III patients. MVD was scored by digital morphometric analysis of CD31-stained whole tumour sections. TSP was scored using haematoxylin-eosin stained slides. Protein expression of HIF1A and VEGFA was determined by immunohistochemical evaluation of tissue microarrays. Results Median MVD was higher in stage III compared to stage II colon cancers (11.1% versus 5.6% CD31-positive tissue area, p <0.001). High MVD in stage II patients tended to be associated with poor disease free survival (DFS) in univariate analysis (p = 0.056). In contrast, high MVD in 5FU-treated stage III patients was associated with better DFS (p = 0.006). Prognostic value for MVD was observed in multivariate analyses for both cancer stages. Conclusions MVD is an independent prognostic factor associated with poor DFS in stage II colon cancer patients, and with better DFS in stage III colon cancer patients treated with adjuvant chemotherapy

Early evaluation of the effectiveness and cost-effectiveness of ctDNA-guided selection for adjuvant chemotherapy in stage II colon cancer

Background: Current patient selection for adjuvant chemotherapy (ACT) after curative surgery for stage II colon cancer (CC) is suboptimal, causing overtreatment of high-risk patients and undertreatment of low-risk patients. Postoperative circulating tumor DNA (ctDNA) could improve patient selection for ACT. Objectives: We conducted an early model-based evaluation of the (cost-)effectiveness of ctDNA-guided selection for ACT in stage II CC in the Netherlands to assess the conditions for cost-effective implementation. Methods: A validated Markov model, simulating 1000 stage II CC patients from diagnosis to death, was supplemented with ctDNA data. Five ACT selection strategies were evaluated: the current guideline (pT4, pMMR), ctDNA-only, and three strategies that combined ctDNA status with pT4 and pMMR status in different ways. For each strategy, the costs, life years, quality-adjusted life years (QALYs), recurrences, and CC deaths were estimated. Sensitivity analyses were performed to assess the impact of the costs of ctDNA testing, strategy adherence, ctDNA as a predictive biomarker, and ctDNA test performance. Results: Model predictions showed that compared to current guidelines, the ctDNA-only strategy was less effective (+2.2% recurrences, −0.016 QALYs), while the combination strategies were more effective (−3.6% recurrences, +0.038 QALYs). The combination strategies were not cost-effective, since the incremental cost-effectiveness ratio was €67,413 per QALY, exceeding the willingness-to-pay threshold of €50,000 per QALY. Sensitivity analyses showed that the combination strategies would be cost-effective if the ctDNA test costs were lower than €1500, or if ctDNA status was predictive of treatment response, or if the ctDNA test performance improved substantially. Conclusion: Adding ctDNA to current high-risk clinicopathological features (pT4 and pMMR) can improve patient selection for ACT and can also potentially be cost-effective. Future studies should investigate the predictive value of post-surgery ctDNA status to accurately evaluate the cost-effectiveness of ctDNA testing for ACT decisions in stage II CC.</p

Tumour break load is a biologically relevant feature of genomic instability with prognostic value in colorectal cancer

BACKGROUND: Clinically implemented prognostic biomarkers are lacking for the 80% of colorectal cancers (CRCs) that exhibit chromosomal instability (CIN). CIN is characterised by chromosome segregation errors and double-strand break repair defects that lead to somatic copy number aberrations (SCNAs) and chromosomal rearrangement-associated structural variants (SVs), respectively. We hypothesise that the number of SVs is a distinct feature of genomic instability and defined a new measure to quantify SVs: the tumour break load (TBL). The present study aimed to characterise the biological impact and clinical relevance of TBL in CRC. METHODS: Disease-free survival and SCNA data were obtained from The Cancer Genome Atlas and two independent CRC studies. TBL was defined as the sum of SCNA-associated SVs. RNA gene expression data of microsatellite stable (MSS) CRC samples were used to train an RNA-based TBL classifier. Dichotomised DNA-based TBL data were used for survival analysis. RESULTS: TBL shows large variation in CRC with poor correlation to tumour mutational burden and fraction of genome altered. TBL impact on tumour biology was illustrated by the high accuracy of classifying cancers in TBL-high and TBL-low (area under the receiver operating characteristic curve [AUC]: 0.88; p < 0.01). High TBL was associated with disease recurrence in 85 stages II-III MSS CRCs from The Cancer Genome Atlas (hazard ratio [HR]: 6.1; p = 0.007) and in two independent validation series of 57 untreated stages II-III (HR: 4.1; p = 0.012) and 74 untreated stage II MSS CRCs (HR: 2.4; p = 0.01). CONCLUSION: TBL is a prognostic biomarker in patients with non-metastatic MSS CRC with great potential to be implemented in routine molecular diagnostics

Expression of the immune modulator secretory leukocyte protease inhibitor (SLPI) in colorectal cancer liver metastases and matched primary tumors is associated with a poorer prognosis

Secretory leukocyte protease inhibitor (SLPI), a pleiotropic protein expressed by healthy intestinal epithelial cells, functions as an inhibitor of NF-κB and neutrophil proteases and exerts antimicrobial activity. We previously showed SLPI suppresses intestinal epithelial chemokine production in response to microbial contact. Increased SLPI expression was recently detected in various types of carcinoma. In addition, accumulating evidence indicates SLPI expression is favorable for tumor cells. In view of these findings and the abundance of SLPI in the colonic epithelium, we hypothesized SLPI promotes colorectal cancer (CRC) growth and metastasis. Here, we aimed to establish wh

Modeling Personalized Adjuvant TreaTment in EaRly stage coloN cancer (PATTERN)

Aim To develop a decision model for the population-level evaluation of strategies to improve the selection of stage II colon cancer (CC) patients who benefit from adjuvant chemotherapy. Methods A Markov cohort model with a one-month cycle length and a lifelong time horizon was developed. Five health states were included; diagnosis, 90-day mortality, death other causes, recurrence and CC death. Data from the Netherlands Cancer Registry were used to parameterize the model. Transition probabilities were estimated using parametric survival models including relevant clinical and pathological covariates. Subsequently, biomarker status was implemented using external data. Treatment effect was incorporated using pooled trial data. Model development, data sources used, parameter estimation, and internal and external validation are described in detail. To illustrate the use of the model, three example strategies were evaluated in which allocation of treatment was based on (A) 100% adherence to the Dutch guidelines, (B) observed adherence to guideline recommendations and (C) a biomarker-driven strategy. Results Overall, the model showed good internal and external validity. Age, tumor growth, tumor sidedness, evaluated lymph nodes, and biomarker status were included as covariates. For the example strategies, the model predicted 83, 87 and 77 CC deaths after 5 years in a cohort of 1000 patients for strategies A, B and C, respectively. Conclusion This model can be used to evaluate strategies for the allocation of adjuvant chemotherapy in stage II CC patients. In future studies, the model will be used to estimate population-level long-term health gain and cost-effectiveness of biomarker-based selection strategies.Financial support for this study was provided by a grant from ZonMw (Grant number: 848015007). ZonMw had no role in designing the study, interpreting the data, writing the manuscript, and publishing the report

Comparison of NTRK fusion detection methods in microsatellite-instability-high metastatic colorectal cancer

Tropomyosin receptor kinase (TRK) inhibitors have been approved for metastatic solid tumors harboring NTRK fusions, but the detection of NTRK fusions is challenging. International guidelines recommend pan-TRK immunohistochemistry (IHC) screening followed by next generation sequencing (NGS) in tumor types with low prevalence of NTRK fusions, including metastatic colorectal cancer (mCRC). RNA-based NGS is preferred, but is expensive, time-consuming, and extracting good-quality RNA from FFPE tissue is challenging. Alternatives in daily clinical practice are warranted. We assessed the diagnostic performance of RNA-NGS, FFPE-targeted locus capture (FFPE-TLC), fluorescence in situ hybridization (FISH), and the 5'/3' imbalance quantitative RT-PCR (qRT-PCR) after IHC screening in 268 patients with microsatellite-instability-high mCRC, the subgroup in which NTRK fusions are most prevalent (1-5%). A consensus result was determined after review of all assay results. In 16 IHC positive tumors, 10 NTRK fusions were detected. In 33 IHC negative samples, no additional transcribed NTRK fusions were found, underscoring the high sensitivity of IHC. Sensitivity of RNA-NGS, FFPE-TLC, FISH, and qRT-PCR was 90%, 90%, 78%, and 100%, respectively. Specificity was 100% for all assays. Robustness, defined as the percentage of samples that provided an interpretable result in the first run, was 100% for FFPE-TLC, yet more limited for RNA-NGS (85%), FISH (70%), and qRT-PCR (70%). Overall, we do not recommend FISH for the detection of NTRK fusions in mCRC due to its low sensitivity and limited robustness. We conclude that RNA-NGS, FFPE-TLC, and qRT-PCR are appropriate assays for NTRK fusion detection, after enrichment with pan-TRK IHC, in routine clinical practice

Modeling Personalized Adjuvant TreaTment in EaRly stage coloN cancer (PATTERN)

Aim: To develop a decision model for the population-level evaluation of strategies to improve the selection of stage II colon cancer (CC) patients who benefit from adjuvant chemotherapy. Methods: A Markov cohort model with a one-month cycle length and a lifelong time horizon was developed. Five health states were included; diagnosis, 90-day mortality, death other causes, recurrence and CC death. Data from the Netherlands Cancer Registry were used to parameterize the model. Transition probabilities were estimated using parametric survival models including relevant clinical and pathological covariates. Subsequently, biomarker status was implemented using external data. Treatment effect was incorporated using pooled trial data. Model development, data sources used, parameter estimation, and internal and external validation are described in detail. To illustrate the use of the model, three example strategies were evaluated in which allocation of treatment was based on (A) 100% adherence to the Dutch guidelines, (B) observed adherence to guideline recommendations and (C) a biomarker-driven strategy. Results: Overall, the model showed good internal and external validity. Age, tumor growth, tumor sidedness, evaluated lymph nodes, and biomarker status were included as covariates. For the example strategies, the model predicted 83, 87 and 77 CC deaths after 5 years in a cohort of 1000 patients for strategies A, B and C, respectively. Conclusion: This model can be used to evaluate strategies for the allocation of adjuvant chemotherapy in stage II CC patients. In future studies, the model will be used to estimate population-level long-term health gain and cost-effectiveness of biomarker-based selection strategies