Data Driven Mathematical Model of FOLFIRI Treatment for Colon Cancer

Many colon cancer patients show resistance to their treatments. Therefore, it is important to consider unique characteristic of each tumor to find the best treatment options for each patient. In this study, we develop a data driven mathematical model for interaction between the tumor microenvironment and FOLFIRI drug agents in colon cancer. Patients are divided into five distinct clusters based on their estimated immune cell fractions obtained from their primary tumors’ gene expression data. We then analyze the effects of drugs on cancer cells and immune cells in each group, and we observe different responses to the FOLFIRI drugs between patients in different immune groups. For instance, patients in cluster 3 with the highest T-reg/T-helper ratio respond better to the FOLFIRI treatment, while patients in cluster 2 with the lowest T-reg/T-helper ratio resist the treatment. Moreover, we use ROC curve to validate the model using the tumor status of the patients at their follow up, and the model predicts well for the earlier follow up days.

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Data Driven Mathematical Model of Colon Cancer Progression

Journal of Clinical Medicine ◽

10.3390/jcm9123947 ◽

2020 ◽

Vol 9 (12) ◽

pp. 3947

Author(s):

Arkadz Kirshtein ◽

Shaya Akbarinejad ◽

Wenrui Hao ◽

Trang Le ◽

Sumeyye Su ◽

...

Keyword(s):

Mathematical Model ◽

Colon Cancer ◽

Cancer Progression ◽

Immune Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Data Driven ◽

Colon Cancer Progression ◽

Negative Effect

Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients the size of the tumor is sensitive to the parameters related to macrophages, such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.

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Data driven mathematical model of colon cancer progression

10.1101/2020.11.02.365668 ◽

2020 ◽

Author(s):

Arkadz Kirshtein ◽

Shaya Akbarinejad ◽

Wenrui Hao ◽

Trang Le ◽

Rachel A. Aronow ◽

...

Keyword(s):

Mathematical Model ◽

Colon Cancer ◽

Cancer Progression ◽

Immune Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Data Driven ◽

Colon Cancer Progression ◽

Negative Effect

AbstractEvery colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients, the size of the tumor is sensitive to the parameters related to macrophages such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.

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Establishment and Validation of a Genetic Label Associated With M2 Macrophage Infiltration to Predict Survival in Colon Cancer Patients and Assist Immunotherapy

10.21203/rs.3.rs-712255/v1 ◽

2021 ◽

Author(s):

Boyang Xu ◽

Ziqi Peng ◽

Guanyu Yan ◽

Ningning Wang ◽

Moye Chen ◽

...

Keyword(s):

Colon Cancer ◽

Microsatellite Instability ◽

Cancer Patients ◽

Immune Cell ◽

Checkpoint Inhibitors ◽

Macrophage Infiltration ◽

M2 Macrophage ◽

High Morbidity ◽

M2 Macrophages ◽

Hub Genes

Abstract Background: Colon cancer is a kind of malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divide it into different subtypes in order to achieve individualized treatment. With the rise of immunotherapy, its value in the field of tumor has initially emerged. Based on the above background, from the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored it.Methods: Cibersort was used to analyze the level of immune cell infiltration in colon cancer patients in the TCGA database. WGCNA, Consensus Clustering analysis, Lasso analysis, and univariate KM analysis were used to screen and verify the hub genes associated with M2 macrophages. PCA was used to establish the M2 macrophage-related score—M2I Score. The correlation between M2I Score and somatic cell variation and microsatellite instability were analysed. Furthermore the correlation between M2 macrophage score and differences in immunotherapy sensitivity was also explored. Results: M2 macrophage infiltration was associated with poor prognosis. Four hub genes (ANKS4B, CTSD, TIMP1, and ZNF703) were selected as the progression-related genes associated with M2 macrophages. A stable and accurate M2I Score for M2 macrophages used in COAD was constructed based on four hub genes. M2I Score was positively correlated with tumor mutation load (TMB). The M2I Score of MSI-H group was higher than that of MSI-L group and MSS group. Combine with the TCIA database, we concluded that patients with a high M2I Score were more sensitive to PD-1 inhibitors and PD-1 inhibitors combined with CTLA-4 inhibitors. The low rating group may have better efficacy without immune checkpoint inhibitors or with CTLA4 inhibitors alone.Conclusion: Four prognostic hub genes associated with M2 macrophages were screened to establish the M2I Score and divided the patients into two subgroups: high M2I Score group and low M2I Score group. TMB, microsatellite instability and sensitivity to immunotherapy were higher in the high-rated group. PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors are preferred for patients in the high-rated group who are more sensitive to immunotherapy.

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Determinants of health-related quality of life in colon cancer patients include stoma and smoking habits, but not type of surgery

10.21203/rs.2.13676/v1 ◽

2019 ◽

Author(s):

Catarina Tiselius ◽

Andreas Rosenblad ◽

Eva Strand ◽

Kennet Smedh

Keyword(s):

Quality Of Life ◽

Colon Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Linear Regression Analysis ◽

Health Related Quality ◽

Related Quality ◽

Health Related

Abstract Background: Health-related quality of life (HRQoL) has gained increased attention in cancer care. Studies have shown that poor QoL might worsen the cancer related prognosis. The aim of this study was to investigate HRQoL in patients with colon cancer and to compare data with reference values from the general population in Sweden at diagnosis (baseline) and at six months of follow-up. Methods : This was a prospective population-based study of colon cancer patients from Västmanland County, Sweden, included between March 2012 and September 2016. HRQoL was measured using the cancer-specific EORTC QLQ-C30 questionnaire. Data on HRQoL was compared with Swedish population reference values. Multiple linear regression analysis adjusted for age, sex, body mass index (BMI), American Society of Anaesthesiology (ASA) physical status classification, emergency/elective surgery, and resection with/without a stoma and tumour stage (TNM), was used. Results : A total of 67% (376/561) of all incident colon cancer patients (196 [52.1%] females) were included. Mean (range) age was 73 (30-96) years. The univariate analysis showed that patients with colon cancer had worse QoL (8/15 parameters) compared with a Swedish reference population both at baseline and at 6 months follow-up. Furthermore, linear regression analysis showed that patients with more comorbidity (ASA 3 and 4), smokers and patients planned to be operated on with a stoma, were at higher risks for poor QoL than the other included patients. Conclusions : The reported determinants of HRQoL may be used to identify risk groups and enable individualized care for patients that need more support from health care.

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QIM19-124: Does Virtual Navigation Improve Care Continuity and Compliance in Patients With Early Stage Colon Cancer?

Journal of the National Comprehensive Cancer Network ◽

10.6004/jnccn.2018.7127 ◽

2019 ◽

Vol 17 (3.5) ◽

pp. QIM19-124

Author(s):

Dayna Crawford ◽

Brook Blackmore ◽

Jeremy Ortega ◽

Erica Williams

Keyword(s):

Colon Cancer ◽

Cancer Patients ◽

Early Stage ◽

Stage I ◽

Stage Iii ◽

Nccn Guidelines ◽

Care Continuity ◽

Colon Cancers ◽

Follow Up Care

Background: Colon cancer is the 3rd most common cancer in men and women combined, with an occurrence rate of 4.49% for men and 4.15% for women. The 2018 expectation is 50,630 deaths related to colon cancer in the United States (American Cancer Society Facts and Figures 2018). Early detection is increasing with nearly 45% of colon cancers diagnosed as stage I/II (Sarah Cannon Cancer Registry 2015). Treatment for early stage I/II colon cancer patients usually involves surgery then surveillance. On-site navigators perform their duties by patient need and barriers to care. Late stage III/IV colon cancer patients require more assistance and face more barriers, which often leaves early stage I/II patients without an advocate. This disparity can lead to lower rates of follow-up care for early stage I/II patients. Sarah Cannon created a program for virtual colon navigation (VCN) to determine if early stage I/II patients benefit from a virtual navigator who offers support by phone throughout their disease process. Objectives: The goal was to increase early stage I/II patients’ knowledge of their cancer and convey the importance of compliance with follow-up care, such as repeat colonoscopy as recommended by their physician and NCCN Guidelines. Methods: By developing software that utilizes artificial intelligence, Sarah Cannon created an automated process to identify colon cancer patients at the time of diagnosis. This technology then routes positive pathology reports to a VCN who contacts the early stage I/II patients by telephone, ensuring patient connection to the suitable physician for treatment. The VCN helps patients understand their diagnosis, provides education, assesses barriers to care, connects to resources, provides emotional support, and offers assistance with follow-up for physician visits, imaging and procedures such as colonoscopies, based upon NCCN Guidelines and physician guidelines. The VCN also connects stage III/IV patients with an on-site navigator in their region for more hands-on navigation. Results: Through September 2018, Sarah Cannon navigated 734 colon cancers, 332 stage I/II and 402 stage III/IV. With our increased capacity, Sarah Cannon/HCA maintained a 98% rate of follow-up care with new diagnoses of all stages of colon cancer. Conclusions: The VCN program allowed Sarah Cannon/HCA to improve care continuity and compliance based upon NCCN Guidelines for early stage I/II colon cancer patients throughout 5 regions and 37 facilities.

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Validation of a genomic classifier (ColoPrint) for predicting outcome in the T3-MSS subgroup of stage II colon cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.3510 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 3510-3510 ◽

Cited By ~ 3

Author(s):

Ramon Salazar ◽

Josep Tabernero ◽

Victor Moreno ◽

Ulrich Nitsche ◽

Thomas Bachleitner-Hofmann ◽

...

Keyword(s):

Colon Cancer ◽

High Risk ◽

Adjuvant Therapy ◽

Cancer Patients ◽

Lower Risk ◽

Low Risk ◽

Stage Ii ◽

Disease Relapse ◽

Risk Of Disease

3510 Background: Adjuvant therapy for stage II patients is recommended for patients with high risk features, especially with T4 tumors. Adjuvant therapy is not indicated for patients with MSI-H status who are considered of being at low risk of disease relapse. However, this leaves the majority of patients with an undetermined risk. ColoPrint is an 18-gene expression classifier that identifies early-stage colon cancer patients at higher risk of disease relapse. Methods: ColoPrint was developed using whole genome expression data and was validated in public datasets (n=322) and independent patient cohorts from 5 European hospitals. Tissue specimen, clinical parameters, MSI-status and follow-up data (median follow-up 70 months) for patients were available and the ColoPrint index was determined using validated diagnostic arrays. Uni-and multivariate analysis was performed on the pooled stage II patient set (n=320) and the subset of patients who were T3/ MSS (n=227). Results: In the analysis of all stage II patients, ColoPrint classified two-third of stage II patients as being at lower risk. The 3-year Relapse-Free-Survial (RFS) RFS was 91% for Low Risk and 74% for patients at higher risk with a HR of 2.9 (p=0.001). Clinicopathological parameters from the ASCO recommendations (T4, perforation, <12 LN assessed, and/ or high grade) or NCCN guidelines (ASCO factors plus angio-lymphatic invasion) did not predict a differential outcome for high risk patients (p< 0.20). In the subgroup of patients with T3 and MSS phenotype, ColoPrint classified 61% of patients at lower risk with a 3-year RFS of 91% (86-96%) and 39% of patients at higher risk with a 3-year RFS of 73% (63-83%) (p=0.002). No clinical parameter was significantly prognostic in this subgroup. Conclusions: ColoPrint combined with established clinicopathological factors and MSI, significantly improves prognostic accuracy, thereby facilitating the identification of patients at higher risk who might be considered for additional treatment.

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Genomic classifiers (ColoPrint/MSI-Print) predict outcome and chemotherapy benefit in stage II and III colon cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.4_suppl.378 ◽

2013 ◽

Vol 31 (4_suppl) ◽

pp. 378-378 ◽

Cited By ~ 2

Author(s):

Scott Kopetz ◽

Zhi-Qin Jiang ◽

Michael J. Overman ◽

Christa Dreezen ◽

Sun Tian ◽

...

Keyword(s):

Colon Cancer ◽

High Risk ◽

Adjuvant Chemotherapy ◽

Cancer Patients ◽

Additional Treatment ◽

Low Risk ◽

Stage Ii ◽

High Risk Patients ◽

Risk Patients

378 Background: Although the benefit of chemotherapy in stage II and III colon cancer patients is significant, many patients might not need adjuvant chemotherapy because they have a good prognosis even without additional treatment. ColoPrint is a gene expression classifier that distinguish patients with low or high risk of disease relapse. It was developed using whole genome expression data and has been validated in public datasets, independent European patient cohorts and technical studies (Salazar 2011 JCO, Maak 2012 Ann Surg). Methods: In this study, the commercial ColoPrint test was validated in stage II (n=96) and III patients (n=95) treated at the MD Anderson Cancer Center from 2003 to 2009. Frozen tissue specimen, clinical parameters, MSI-status and follow-up data (median follow-up 64 months) were available. The 64-gene MSI-signature developed to identify patients with deficient mismatch repair system (Tian 2012 J Path) was evaluated for its accuracy to identify MSI patients and also for prognosis. Results: In this cohort, ColoPrint classified 56% of stage II and III patients as being at low risk. The 3-year Relapse-Free-Survival (RFS) was 90.6% for Low Risk and 78.4% for High Risk patients with a HR of 2.33 (p=0.025). In uni-and multivariate analysis ColoPrint and stage were the only significant factors to predict outcome. The MSI-signature classified 47 patients (24.6%) as MSI-H and most MSI-H patients were ColoPrint low risk (81%). Patients who were ColoPrint low risk and MSI-H by signature had the best outcome with a 3-year RFS of 95% while patients with ColoPrint high risk had a worse outcome independently of the MSI-status. Low risk ColoPrint patients had a good outcome independent of stage or chemotherapy treatment (90.1% 3-year RFS for treated patients, 91.4% for untreated patients) while ColoPrint high risk patients treated with adjuvant chemotherapy had 3-year RFS of 84%, compared to 70.1% 3-year RFS in untreated patients (p=0.026). Conclusions: The combination of ColoPrint and MSI-Print improves the prognostic accuracy in stage II and stage III patients and may help the identification of patients at higher risk who are more likely to benefit from additional treatment

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Long-term follow-up of bone marrow micrometastases in colon cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2015.33.15_suppl.3529 ◽

2015 ◽

Vol 33 (15_suppl) ◽

pp. 3529-3529

Author(s):

Carsten T. Viehl ◽

Ulrich Guller ◽

Michaela Ramser ◽

Salome Dell-Kuster ◽

Benjamin Weixler ◽

...

Keyword(s):

Bone Marrow ◽

Colon Cancer ◽

Cancer Patients ◽

Bone Marrow Micrometastases ◽

Long Term Follow Up

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The Chemokine CXCL16 Is a New Biomarker for Lymph Node Analysis of Colon Cancer Outcome

International Journal of Molecular Sciences ◽

10.3390/ijms20225793 ◽

2019 ◽

Vol 20 (22) ◽

pp. 5793 ◽

Cited By ~ 3

Author(s):

Manar AbdelMageed ◽

Haytham Ali ◽

Lina Olsson ◽

Gudrun Lindmark ◽

Marie-Louise Hammarström ◽

...

Keyword(s):

Colon Cancer ◽

Protein Expression ◽

Lymph Nodes ◽

Cancer Patients ◽

Mrna Levels ◽

Colon Tissue ◽

Regional Lymph Nodes ◽

Primary Tumors ◽

Kaplan Meier ◽

Cancer Outcome

Chemokines are important in the development and progression of tumors. We investigated the expression of CXCL14 and CXCL16 in colon cancer. Expression of mRNA was assessed in primary tumors and lymph nodes and CXCL16 mRNA levels were correlated to patient’s survival. Protein expression was investigated by two-color immunofluorescence and immunomorphometry. CXCL14 and CXCL16 mRNA levels and protein expression were significantly higher in colon cancer primary tumors compared to apparently normal colon tissue. Positive cells were tumor cells, as revealed by anti-CEA and anti-EpCAM staining. CXCL16, but not CXCL14, mRNA levels were significantly higher in hematoxylin and eosin positive (H&E(+)) compared to H&E(−) colon cancer lymph nodes or control nodes (P < 0.0001). CXCL16 mRNA was expressed in 5/5 colon cancer cell lines while CXCL14 was expressed significantly in only one. Kaplan-Meier analysis revealed that colon cancer patients with lymph nodes expressing high or very high levels (7.2 and 11.4 copies/18S rRNA unit, respectively) of CXCL16 mRNA had a decreased mean survival time of 30 and 46 months at the 12-year follow-up (P = 0.04, P = 0.005, respectively). In conclusion, high expression of CXCL16 mRNA in regional lymph nodes of colon cancer patients is a sign of a poor prognosis.

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Intratumoral immune reaction: A novel paradigm for cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.4_suppl.471 ◽

2011 ◽

Vol 29 (4_suppl) ◽

pp. 471-471 ◽

Cited By ~ 1

Author(s):

J. Galon ◽

B. Mlecnik ◽

A. Kirilovsky ◽

G. Bindea ◽

M. Tosolini ◽

...

Keyword(s):

Colorectal Cancer ◽

T Cells ◽

Cancer Patients ◽

Immune Reaction ◽

Immune Cell ◽

Human Cancer ◽

Effector Memory ◽

Tumor Escape ◽

Primary Tumors ◽

The Impact

471 Background: To date the anatomic extent of tumor (TNM classifications) has been by far the most important factors to predict the prognosis of cancer patients. However, the impact of immune responses and tumor escape on patient prognosis in human cancer is poorly understood. Methods: We analyzed large retrospective cohorts of colorectal cancer patients. Results: We showed that tumors from human colorectal cancer with a high density of infiltrating memory and effector memory T-cells (T-EM) are less likely to disseminate to lymphovascular and perineural structures and to regional lymph-nodes (New Engl J Med, 2005). We showed that the combination of immune parameters associating the nature, the density, the functional orientation and the location of immune cells within the tumor was essential to accurately define the impact of the local host immune reaction on patients prognosis (Science, 2006). We proposed to define these immune criteria as “immune contexture.” Analysis of patients with early-stage colorectal cancer confirmed the major role of cyotoxic effector T cells in predicting the prognosis of the patients (J Clin Oncol, 2009). Investigation of the primary tumor microenvironment allowed us to uncover the association of favorable outcomes with efficient coordination of the intratumoral immune response. We described four major immune coordination profiles within primary tumors depending on the balance between tumor escape and immune coordination. Recent advances analyzing mechanisms responsible for lymphocytic infiltration will be discussed. Conclusions: The density and the immune-cell location within the tumor have a prognostic value that is superior of those of the TNM classifications. Tumor invasion is statistically dependent on the host-immune reaction. No significant financial relationships to disclose.

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