Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT.

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

The Inherited KRAS-variant as a Biomarker of Cetuximab Response in NSCLC.

PURPOSE: RTOG 0617 was a phase III randomized trial for patients with unresectable stage IIIA/IIIB non-small cell lung cancer comparing standard-dose (60 Gy) versus high-dose (74 Gy) radiotherapy and chemotherapy, plus or minus cetuximab. Although the study was negative, based on prior evidence that patients with the KRAS-variant, an inherited germline mutation, benefit from cetuximab, we evaluated KRAS-variant patients in RTOG 0617. EXPERIMENTAL DESIGN: From RTOG 0617, 328 of 496 (66%) of patients were included in this analysis. For time-to-event outcomes, stratified log-rank tests and multivariable Cox regression models were used. For binary outcomes, Cochran-Mantel-Haenzel tests and multivariable logistic regression models were used. All statistical tests were two sided, and a P value <0.05 was considered significant. RESULTS: A total of 17.1% (56/328) of patients had the KRAS-variant, and overall survival rates were similar between KRAS-variant and non-variant patients. However, there was a time-dependent effect of cetuximab seen only in KRAS-variant patients-while the hazard of death was higher in cetuximab-treated patients within year 1 [HR = 3.37, 95% confidence interval (CI): 1.13-10.10, P = 0.030], death was lower from year 1 to 4 (HR = 0.33, 95% CI: 0.11-0.97, P = 0.043). In contrast, in non-variant patients, the addition of cetuximab significantly increased local failure (HR = 1.59, 95% CI: 1.11-2.28, P = 0.012). CONCLUSIONS/DISCUSSION: Although an overall survival advantage was not achieved in KRAS-variant patients, there is potential impact of cetuximab for this genetic subset of patients. In contrast, cetuximab seems to harm non-variant patients. These findings further support the importance of genetic patient selection in trials studying the addition of systemic agents to radiotherapy. SIGNIFICANCE: The KRAS-variant is the first functional, inherited miRNA-disrupting variant identified in cancer. Our findings support that cetuximab has a potentially beneficial impact on KRAS-variant patients treated with radiation. The work confirms prior evidence that KRAS-variant patients are a subgroup who are especially sensitive to radiation. These findings further support the potential of this class of variants to enable true treatment personalization, considering the equally important endpoints of response and toxicity.

Application of a genetic signature of late GU toxicity in SCIMITAR, a Post-op SBRT trial.

Predictors of genitourinary toxicity after post-prostatectomy radiotherapy remain elusive. A previously defined germline DNA signature (PROSTOX) has shown predictive ability for late grade ≥ 2 GU toxicity after intact prostate stereotactic body radiotherapy. We explore whether PROSTOX would predict toxicity among patients receiving post-prostatectomy SBRT on a phase II clinical trial.

Transcriptomic Heterogeneity in High-risk Prostate Cancer and Implications for Extraprostatic Disease at Presentation on Prostate-specific Membrane Antigen Positron Emission Tomography.

Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) has greater specificity and sensitivity for detection of extraprostatic prostate cancer (PCa) at presentation than conventional imaging. Although the long-term clinical significance of acting on these findings is unknown, it has been shown that the risk of upstaging is prognostic for long-term outcomes in men with high-risk (HR) or very high-risk (VHR) PCa. We evaluated the association between the risk of upstaging on PSMA PET and the Decipher genomic classifier score, a known prognostic biomarker in localized PCa that is being evaluated for its predictive ability to direct systemic therapy intensification. In a cohort of 4625 patients with HR or VHR PCa, the risk of upstaging on PSMA PET was significantly correlated with the Decipher score (p < 0.001). These results should be seen as hypothesis-generating and warrant further studies on the causal pathways linking PSMA findings, Decipher scores, extraprostatic disease, and long-term clinical outcomes. PATIENT SUMMARY: We found significant correlation between the risk of having prostate cancer outside the prostate gland on a sensitive scan (based on prostate-specific membrane antigen [PSMA]) at initial staging and the Decipher genetic score. The results warrant further studies on the causal pathways between PSMA scan findings, Decipher scores, disease outside the prostate, and long-term outcomes.

Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer.

BACKGROUND: In the era of molecular stratification and effective multimodality therapies, surgical staging of the axilla is becoming less relevant for patients with estrogen receptor (ER)-positive early-stage breast cancer (EBC). Therefore, a nonsurgical method for accurately predicting lymph node disease is the next step in the de-escalation of axillary surgery. This study sought to identify epigenetic signatures in the primary tumor that accurately predict lymph node status. PATIENTS AND METHODS: We selected a cohort of patients in The Cancer Genome Atlas (TCGA) with ER-positive, HER2-negative invasive ductal carcinomas, and clinically-negative axillae (n = 127). Clinicopathological nomograms from the Memorial Sloan Kettering Cancer Center (MSKCC) and the MD Anderson Cancer Center (MDACC) were calculated. DNA methylation (DNAm) patterns from primary tumor specimens were compared between patients with pN0 and those with > pN0. The cohort was divided into training (n = 85) and validation (n = 42) sets. Random forest was employed to obtain the combinations of DNAm features with the highest accuracy for stratifying patients with > pN0. The most efficient combinations were selected according to the area under the curve (AUC). RESULTS: Clinicopathological models displayed a modest predictive potential for identifying > pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with > pN0 were identified (n = 1656). DMS showed a similar performance to the MSKCC model (AUC = 0.76, p = 0.83). Machine learning approaches generated five epigenetic classifiers, which showed higher discriminative potential than the clinicopathological variables tested (AUC > 0.88, p < 0.05). CONCLUSIONS: Epigenetic classifiers based on primary tumor characteristics can efficiently stratify patients with no lymph node involvement from those with axillary lymph node disease, thereby providing an accurate method of staging the axilla.

Viral Burden and Clearance in Asymptomatic COVID-19 Patients.

Background: Containing coronavirus disease 2019 (COVID-19) has been difficult, due to both the large number of asymptomatic infected individuals and the long duration of infection. Managing these challenges requires understanding of the differences between asymptomatic vs symptomatic patients and those with a longer duration of infectivity. Methods: Individuals from Los Angeles were tested for COVID-19, and a group positive for COVID-19 chose to have follow-up testing. Associations between symptoms and demographic factors, viral burden measured by cycle threshold (CT) value, and duration of polymerase chain reaction (PCR) positivity were analyzed. Results: Eighteen point eight percent of patients were positive for COVID-19. Asymptomatic COVID-19-positive patients were significantly younger than symptomatic patients (2.6 years; P < .001). There were no differences in average CT between asymptomatic and symptomatic patients. The estimated median duration of COVID-19 PCR positivity was 23 days. Being asymptomatic throughout the course of infection was the only factor associated with a shorter course of COVID-19 PCR positivity (21 vs 28 days; P = .002). Conclusions: We found important differences and similarities between asymptomatic and symptomatic COVID-19-positive patients, the most meaningful being a similar level of virus as measured by PCR, but a shorter duration of PCR positivity for asymptomatic patients. These findings suggest that asymptomatic patients may have more efficient clearance of virus, which may be relevant for management and screening.

Epigenetic Signatures Predict Pathologic Nodal Stage in Breast Cancer Patients with Estrogen Receptor-Positive, Clinically Node-Positive Disease.

BACKGROUND: Breast cancer patients with clinically positive nodes who undergo upfront surgery are often recommended for axillary lymph node dissection (ALND), yet more than half are found to have limited nodal disease (≤ 3 positive nodes, pN1) at surgery. In this study, we examined the efficiency of molecular classifiers in stratifying patients with clinically positive nodes to pN1 versus > pN1 disease. METHODS: We evaluated the clinical and epigenetic data of patients in The Cancer Genome Atlas with estrogen receptor-positive, human epidermal growth factor receptor 2-negative invasive ductal carcinoma who underwent ALND for node-positive disease. Patients were divided into control (pN1, ≤ 3 positive nodes) and case (> pN1, > 3 positive nodes) groups. Machine learning algorithms were trained on 50% of the cohort and validated on the remaining 50% to identify DNA methylation signatures that predict > pN1 disease. Clinical variables and epigenetic signatures were compared. RESULTS: Controls (n = 34) and case (n = 24) cohorts showed similar mean age (56.4 ± 12.2 vs. 57.6 ± 16.7 years; p = 0.77), number of nodes removed (16.1 ± 7.3 vs. 17.5 ± 6.2; p = 0.45), tumor grade (p = 0.76), presence of lymphovascular invasion (p = 0.18), extranodal extension (p = 0.17), tumor laterality (p = 0.89), and tumor location (p = 0.42). The mean number of positive nodes was significantly different (1.76 ± 0.82, pN1; 8.83 ± 5.36, > pN1; p < 0.001). Three epigenetic signatures (EpiSig14, EpiSig13, EpiSig10) based on DNA methylation patterns of the primary tumors demonstrated high accuracy in predicting > pN1 disease (area under the curve 0.98). CONCLUSIONS: Epigenetic signatures have an excellent diagnostic accuracy for stratifying nodal disease in patients with clinically positive nodes. Validation of this tool is warranted and may provide an accurate and cost-effective method of identifying patients with predicted low nodal burden who could be spared the morbidity of ALND.

Germline biomarkers predict toxicity to anti-PD1/PDL1 checkpoint therapy.

BACKGROUND: There is great interest in finding ways to identify patients who will develop toxicity to cancer therapies. This has become especially pressing in the era of immune therapy, where toxicity can be long-lasting and life-altering, and primarily comes in the form of immune-related adverse effects (irAEs). Treatment with the first drugs in this class, anti-programmed death 1 (anti-PD1)/programmed death-ligand 1 (PDL1) checkpoint therapies, results in grade 2 or higher irAEs in up to 25%-30% of patients, which occur most commonly within the first 6 months of treatment and can include arthralgias, rash, pruritus, pneumonitis, diarrhea and/or colitis, hepatitis, and endocrinopathies. We tested the hypothesis that germline microRNA pathway functional variants, known to predict altered systemic stress responses to cancer therapies, would predict irAEs in patients across cancer types. METHODS: MicroRNA pathway variants were evaluated for an association with grade 2 or higher toxicity using four classifiers on 62 patients with melanoma, and then the panel's performance was validated on 99 patients with other cancer types. Trained classifiers included classification trees, LASSO-regularized logistic regression, boosted trees, and random forests. Final performance measures were reported on the training set using leave-one-out cross validation and validated on held-out samples. The predicted probability of toxicity was evaluated for its association, if any, with response categories to anti-PD1/PDL1 therapy in the melanoma cohort. RESULTS: A biomarker panel was identified that predicts toxicity with 80% accuracy (F1=0.76, area under the curve (AUC)=0.82) in the melanoma training cohort and 77.6% accuracy (F1=0.621, AUC=0.778) in the pan-cancer validation cohort. In the melanoma cohort, the predictive probability of toxicity was not associated with response categories to anti-PD1/PDL1 therapy (p=0.70). In the same cohort, the most significant biomarker of toxicity in RAC1, predicting a greater than ninefold increased risk of toxicity (p<0.001), was also not associated with response to anti-PD1/PDL1 therapy (p=0.151). CONCLUSIONS: A germline microRNA-based biomarker signature predicts grade 2 and higher irAEs to anti-PD1/PDL1 therapy, regardless of tumor type, in a pan-cancer manner. These findings represent an important step toward personalizing checkpoint therapy, the use of which is growing rapidly.

Germline variants disrupting microRNAs predict long-term genitourinary toxicity after prostate cancer radiation.

BACKGROUND AND PURPOSE: The purpose of this study was to determine whether single nucleotide polymorphisms disrupting microRNA targets (mirSNPs) can serve as predictive biomarkers for toxicity after radiotherapy for prostate cancer and whether these may be differentially predictive depending on radiation fractionation. MATERIALS AND METHODS: We identified 201 men treated with two forms of definitive radiotherapy for prostate cancer at two institutions: 108 men received conventionally-fractionated radiotherapy (CF-RT) and 93 received stereotactic body radiotherapy (SBRT). Germline DNA was evaluated for the presence of functional mirSNPs. Random forest, boosted trees and elastic net models were developed to predict late grade ≥2 GU toxicity by the RTOG scale. RESULTS: The crude incidence of late grade ≥2 GU toxicity was 16% after CF-RT and 15% after SBRT. An elastic net model based on 22 mirSNPs differentiated CF-RT patients at high risk (71.5%) versus low risk (7.5%) for toxicity, with an area under the curve (AUC) values of 0.76-0.81. An elastic net model based on 32 mirSNPs differentiated SBRT patients at high risk (64.7%) versus low risk (3.9%) for toxicity, with an area under the curve (AUC) values of 0.81-0.87. These models were specific to treatment type delivered. Prospective studies are warranted to further validate these results. CONCLUSION: Predictive models using germline mirSNPs have high accuracy for predicting late grade ≥2 GU toxicity after either CF-RT or SBRT, and are unique for each treatment, suggesting that germline predictors of late radiation sensitivity are fractionation-dependent. Prospective studies are warranted to further validate these results.

Development and Validation of a Comprehensive Multivariate Dosimetric Model for Predicting Late Genitourinary Toxicity Following Prostate Cancer Stereotactic Body Radiotherapy.

Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3-98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.

Transcriptomic Heterogeneity of Gleason Grade Group 5 Prostate Cancer.

Gleason grade group (GG) 5 prostate cancer has been associated with an aggressive natural history, and retrospective data support a role for treatment intensification. However, clinical outcomes remain heterogeneous in this cohort, and intensified treatments carry an increased risk of adverse events. We sought to explore the transcriptomic heterogeneity of GG 5 tumors by querying transcriptomic data from the tumors of 2138 patients with GG 5 disease who underwent prostatectomy. Four distinct consensus clusters were identified with respect to differential transcriptional activation of hallmark pathways, with distinct molecular subtyping profiles and different average genomic risks (AGRs). One cluster, accounting for 325 tumors (15.2% of the population), was enriched for genes related to the cell cycle/proliferation, metabolic pathways, androgen response pathways, and DNA repair, and had a higher AGR than the other clusters (p < 0.001). This clustering, with an identification of a high genomic risk cluster, was subsequently validated in a separate cohort of 1921 patients as well as a third cohort of 201 patients. The latter cohort had outcomes available, and it was found that patients in the high genomic risk cluster had significantly worse distant metastasis-free survival than the other clusters. Tumors in this high genomic risk cluster of GG 5 disease may be particularly likely to benefit from treatment intensification. PATIENT SUMMARY: In this report, we examined differences in gene expression in tumors from men with Gleason grade group 5 prostate cancer. We identified significant diversity, with one specific subgroup of tumors associated with expression profiles that suggest a worse prognosis.

A Phase II Trial of 5-Day Neoadjuvant Radiotherapy for Patients with High-Risk Primary Soft Tissue Sarcoma.

PURPOSE: In a single-institution phase II study, we evaluated the safety of a 5-day dose-equivalent neoadjuvant radiotherapy (RT) regimen for high-risk primary soft tissue sarcoma. PATIENTS AND METHODS: Patients received neoadjuvant RT alone (30 Gy in five fractions) to the primary tumor with standard margins. The primary endpoint was grade ≥2 late-radiation toxicity. Major wound complications, local recurrences, and distant metastases were also examined. In exploratory analysis, we evaluated germline biomarkers for wound toxicity and the effects of the study on treatment utilization. RESULTS: Over 2 years, 52 patients were enrolled with median follow-up of 29 months. Seven of 44 evaluable patients (16%) developed grade ≥2 late toxicity. Major wound complications occurred in 16 of 50 patients (32%); a signature defined by 19 germline SNPs in miRNA-binding sites of immune and DNA damage response genes, in addition to lower extremity tumor location, demonstrated strong predictive performance for major wound complications. Compared with the preceding 2-year period, the number of patients treated with neoadjuvant RT alone at our institution increased 3-fold, with a concomitant increase in the catchment area. CONCLUSIONS: A shorter 5-day neoadjuvant RT regimen results in favorable rates of wound complications and grade ≥2 toxicity after 2-year follow-up. Five-day RT significantly increased utilization of neoadjuvant RT at our high-volume sarcoma center. With further validation, a putative germline biomarker for wound complications may guide safer RT utilization.

Identifying MicroRNA Pathway Variants as Biomarkers of Patient Selection for Immune Therapy.

In this chapter we discuss the discovery and validation of microRNA (miRNA) associated germline biomarkers, as well as their application on a cohort of patients treated with immune therapy to predict response and toxicity. MiRNAs are the first class of noncoding RNAs discovered, and these pathways have been shown to be important regulators of the systemic stress response, including that to cancer therapy. We detail the original discovery efforts identifying germline biomarkers that disrupt miRNA circuitry, and then the selection, application, and validation of these biomarkers and their potential to predict important outcomes to checkpoint therapy.

Correction to: Toward a comprehensive view of cancer immune responsiveness: a synopsis from the SITC workshop.

Following publication of the original article [1], the author reported that an author name, Roberta Zappasodi, was missed in the authorship list.

Lack of an Association between a Polymorphism in the KRAS 3' Untranslated Region (rs61764370) and Endometriosis in a Large European Case-Control Study.

BACKGROUND: Endometriosis is a common disorder that affects 6-10% of reproductive age women. In a previous study, we demonstrated that a polymorphism in let-7 microRNA-binding site in the 3' untranslated region of the KRAS gene was found in 31% of subjects with endometriosis resistant to medical therapy. This polymorphism was now tested in a large, case-control study. METHODS: Peripheral blood or peritoneal biopsies from 2,077 European subjects with or without endometriosis and known infertility were tested for the presence of the variant allele using polymerase chain reaction. RESULTS: Histologically proven endometriosis was found in 1,140 subjects, while 937 subjects were disease free. Variant allele carrier rates in subjects with and without endometriosis were 15.7 and 15.1%, respectively. No association between the variant KRAS allele and stage of the disease, age at surgery, body mass index, or type of infertility was identified. CONCLUSION: A germ-line single-nucleotide polymorphism in the let-7 microRNA-binding site of the KRAS gene was not associated with sporadic endometriosis in an infertile Caucasian population in this large case-control study. However, it remains possible that this gene variant may be a marker of treatment resistance. Further studies on the role of this polymorphism in endometriosis are needed.

Toward a comprehensive view of cancer immune responsiveness: a synopsis from the SITC workshop.

Tumor immunology has changed the landscape of cancer treatment. Yet, not all patients benefit as cancer immune responsiveness (CIR) remains a limitation in a considerable proportion of cases. The multifactorial determinants of CIR include the genetic makeup of the patient, the genomic instability central to cancer development, the evolutionary emergence of cancer phenotypes under the influence of immune editing, and external modifiers such as demographics, environment, treatment potency, co-morbidities and cancer-independent alterations including immune homeostasis and polymorphisms in the major and minor histocompatibility molecules, cytokines, and chemokines. Based on the premise that cancer is fundamentally a disorder of the genes arising within a cell biologic process, whose deviations from normality determine the rules of engagement with the host's response, the Society for Immunotherapy of Cancer (SITC) convened a task force of experts from various disciplines including, immunology, oncology, biophysics, structural biology, molecular and cellular biology, genetics, and bioinformatics to address the complexity of CIR from a holistic view. The task force was launched by a workshop held in San Francisco on May 14-15, 2018 aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to uncover mechanisms of CIR. This workshop was a first step toward a second meeting where the focus would be to address the actionability of some of the questions identified by working groups. In this event, five working groups aimed at defining a path to test hypotheses according to their relevance to human cancer and identifying experimental models closest to human biology, which include: 1) Germline-Genetic, 2) Somatic-Genetic and 3) Genomic-Transcriptional contributions to CIR, 4) Determinant(s) of Immunogenic Cell Death that modulate CIR, and 5) Experimental Models that best represent CIR and its conversion to an immune responsive state. This manuscript summarizes the contributions from each group and should be considered as a first milestone in the path toward a more contemporary understanding of CIR. We appreciate that this effort is far from comprehensive and that other relevant aspects related to CIR such as the microbiome, the individual's recombined T cell and B cell receptors, and the metabolic status of cancer and immune cells were not fully included. These and other important factors will be included in future activities of the taskforce. The taskforce will focus on prioritization and specific actionable approach to answer the identified questions and implementing the collaborations in the follow-up workshop, which will be held in Houston on September 4-5, 2019.

The KRAS-variant and its impact on normal breast epithelial cell biology.

MicroRNA (miRNA)-binding site variants in 3' untranslated regions (3'UTRs) are a novel class of germ-line, functional mutations, which are now recognized as powerful biomarkers of human cancer risk and biology. The first mutation discovered in this class is the KRAS-variant, a let-7-binding site mutation in the 3'UTR of the KRAS oncogene. The KRAS-variant predicts increased cancer risk for certain populations, is a predictive biomarker of cancer treatment response across cancer types, leads to conserved tumor biology and elevated AKT signaling in KRAS-variant patient tumors, and was recently found to predict elevated TGF-ß and immunosuppression in cancer patients. Based on the functional biology of the KRAS-variant in cancer patients, here we chose to investigate altered normal cellular biology in the presence of the KRAS-variant, through interrogation of an isogenic normal breast epithelial cell line model with and without the KRAS-variant. We find that KRAS-variant normal breast epithelial cells exhibit a mesenchymal phenotype, which appears to be due to numerous molecular changes, including miRNA dysregulation and autocrine pathway alterations, including elevated TGF-ß, resulting in ZEB and SNAIL upregulation. Our findings support the hypothesis that the KRAS-variant has a fundamental biological impact on normal cellular biology, that is conserved in these patients when they develop cancer.