Multidisciplinary Cancer Investigation
Volume:1 Issue: 2, Apr 2017

Despite diagnostic, preventive and therapeutic advances, growing incidence of cancer and high rate of mortality among patients affected by specific cancer types indicate current clinical measures are not ideally useful in eradicating cancer. Chemoresistance and subsequent disease relapse are believed to be mainly driven by the cell-molecular heterogeneity of human tumors that necessitates personalized approaches to deal with uniquely complex genetic profile of each patient’s tumor. Such personalized medicinal therapies require dissection of cancer molecular profiles in order to profoundly understand mechanisms underlying drug resistance and disease recurrence. Technological advances in comparative genome sequencing have begun to result in identification of common somatic mutations in specific cancer subtypes that potentially constitute bases for prognostic and diagnostic biomarkers and present novel therapeutic targets. These targets have to be tested in reliable platforms so data of drug responses obtained can be correlated with those responses elicited in origin by the parental tumor itself. Here, I review different models of cancer in vitro and in vivo and outline the utility of these models in drug discovery and novel therapies of cancer with prospect for developing personalized anti-cancer strategies.

Gail model is one of the most important models for the evaluation of breast cancer risk between US white females. According to genetic diversity, there is a possibility of affecting the efficiency of the Gail model in risk assessment of breast cancer among Iranian populations. In this study, the Gail model efficiency in specifying the risk of breast cancer in Iranian population was evaluated.
This was a case-control study. The case group was formed of the referrals to Breast Cancer Research Center, Academic Center for Education Culture and Research (ACECR), who were affected by different types of aggressive cancer.
A total of 416 patients with breast cancer and the same number in the control group were considered during the study. There were no meaningful statistical differences in age at menarche, age at first live birth, and nulliparous women between case and control groups. The average of five-year risk of breast cancer in the case and control groups had no statistically significant difference. Chemoprevention was only eligible for 7.2% of the patients based on 1.67% five-year risk. In addition, there was no statistically meaningful difference between comparative risk and breast cancer risk in a lifetime.
The low risks estimated by the Gail model among patients with breast cancer as well as the absence of meaningful statistical difference in the estimated risks by this model between the case and control groups showed that the Gail model had insufficient efficiency in determining breast cancer risk in the Iranian society.

The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection.
A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used. First, the risk factors were classified into three priorities according to their importance level, were fuzzified and the subtractive clustering method was employed for inputting them with the same order. Randomly, the dataset was divided into two groups of 70 and 30 percent of the total records, and used for training and testing the new model respectively. After the training, the system was separately tested with the Wisconsin and real Clinic's data, and the results were reported.
The desired fuzzy functions were defined for the variables, and the model was trained with the combined dataset. The testing was then conducted first with 30 percent of that dataset, then with the real data obtained from a real Clinic (BCRC) data, while the model's precision for the above stages was 81(sensivity=85.1%, specifity=74.5%) and 84.5 percent (sensivity=89.3%, specifity=79.9%) respectively.
A final ANFIS model was developed and tested for two standard and real datasets on breast cancer. The resulting model could be employed with high precision for the BCRC Clinic's database, as well as conducting similar studies and re-evaluating other databases.

Of the factors leading to false negative results in mammography is breast tissue density, and by increasing the density of breast tissue in mammography, the mammography sensitivity decreases. On the other hand, increasing the breast tissue density leads to increased risk of breast cancer. Various factors such as inheritance, genetics, hormonal and reproductive factors, and nutritional factors can affect the breast density, but the definitive cause of this issue is unknown. This study investigated the possible factors influencing the increase in breast tissue density in mammography.
In this cross-sectional study, a total of 428 patients were enrolled; the demographic questionnaire on variables included age, body mass index (BMI), fertility, age of menarche, taking oral contraceptive pills (OCP), menstrual status and breast size, which were completed and evaluated. To determine the relationship between the studied variables and the breast density in mammography, chi-squared test and logistic regression were used.
The results showed significant correlation in age of less than 50 years, small breast cup size, low BMI, and the premenopausal status with dense view in mammography (P 0.05).
Higher breast density in premenopausal women with low BMI may lower the sensitivity of mammography. This can underscore the importance and necessity of further controls with short intervals as well as the use of other tools for diagnosing breast cancer in these groups of patients.

Primary gastric lymphoma (PGL) is a rare tumor, whose differential diagnosis may become complicated without precise immunohistochemistry (IHC) and genetic analysis.
A 33-year-old woman presented with gastric cancer and had undergone gastrectomy without precise IHC and staging in another center. Inappropriate IHC after surgery showed diffuse large B-cell lymphoma. After her admission to our center, due to gastrointestinal (GI) symptoms further evaluations were performed, leading to application of chemotherapy and radiotherapy regimens. In the next admissions, involvement of tibia, jaw, and gingiva took place and Burkitt lymphoma was diagnosed with precise IHC panel, which made alteration in the treatment. In the last admission, she expired due to renal failure and tumor lysis syndrome.
There was some mismanagement in this case, especially incomplete and inappropriate IHC panel, which led to wrong diagnosis.