Totonchi, M. | Search bioRxiv

bioRxiv2021

Genomic Alterations and Possible Druggable Mutations in Carcinoma of Unknown Primary (CUP)

Carcinoma of Unknown Primary (CUP) is a heterogeneous and metastatic disease where the primary site of origin is undetectable. Currently, chemotherapy is an only state-of-art treatment option for the CUP patient. Employing molecular profiling of the tumour, particularly mutation detection, offers a new treatment for CUP in a personalized fashion. Here, we analyzed mutation and copy number alterations profile of 1,709 CUP samples deposited in GENIE cohort and explored potential druggable mutations. We identified 52 significant mutated genes (SMG) among CUP samples, of which 13 (25%) of SMG were potentially targetable with drugs reproved for the know primary tumour or undergoing clinical trials. The most variants detected were TP53 (43%), KRAS (19.90%), KMT2D (12.60%), and CDKN2A (10.30%). Additionally, the presence of similar variants of TERT promoter in CUP compared to NSCLC samples suggests these mutations may serve as a diagnostic marker for identifying the primary tumour in CUP. Taken together, analyzing mutation profiling of the CUP tumours may open a new way of identifying druggable targets and consequently administrating appropriate treatment in a personalized manner.

cancer biology↗

bioRxiv2018

Breast Cancer Histopathological Image Classification: A Deep Learning Approach

Breast cancer remains the most common type of cancer and the leading cause of cancer-induced mortality among women with 2.4 million new cases diagnosed and 523,000 deaths per year. Historically, a diagnosis has been initially performed using clinical screening followed by histopathological analysis. Automated classification of cancers using histopathological images is a chciteallenging task of accurate detection of tumor sub-types. This process could be facilitated by machine learning approaches, which may be more reliable and economical compared to conventional methods.\n\nTo prove this principle, we applied fine-tuned pre-trained deep neural networks. To test the approach we first classify different cancer types using 6, 402 tissue micro-arrays (TMAs) training samples. Our framework accurately detected on average 99.8% of the four cancer types including breast, bladder, lung and lymphoma using the ResNet V1 50 pre-trained model. Then, for classification of breast cancer sub-types this approach was applied to 7,909 images from the BreakHis database. In the next step, ResNet V1 152 classified benign and malignant breast cancers with an accuracy of 98.7%. In addition, ResNet V1 50 and ResNet V1 152 categorized either benign- (adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma) or malignant- (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma) sub-types with 94.8% and 96.4% accuracy, respectively. The confusion matrices revealed high sensitivity values of 1, 0.995 and 0.993 for cancer types, as well as malignant- and benign sub-types respectively. The areas under the curve (AUC) scores were 0.996,0.973 and 0.996 for cancer types, malignant and benign sub-types, respectively. Overall, our results show negligible false negative (on average 3.7 samples) and false positive (on average 2 samples) results among different models. Availability: Source codes, guidelines and data sets are temporarily available on google drive upon request before moving to a permanent GitHub repository.

bioinformatics↗