Mathematical and Computational Oncology

In this presentation that partly subsumes and summarises in the form of adapted excerpts some recent articles of which I am author or co-author  [2, 3, 11], I suggest that cancer is fundamentally a disease of the control of cell differentiation in multicellular organisms, uncontrolled cell proliferation being a mere consequence of blockade, or unbalance, of cell differentiations. Cancer cell populations, that can reverse the sense of differentiations, are extremely plastic and able to adapt without mutations their phenotypes in order to transiently resist drug insults  [10], which is likely due to the reactivation of ancient, normally silenced, genes  [5, 7, 12]. Stepping from mathematical models of non genetic plasticity in cancer cell populations  [4, 6] and questions they raise, I propose an evolutionary biology approach to shed light on this problem a) from a theoretical viewpoint by a description of multicellular organisms in terms of multi-level structures, which integrate function and matter from lower to upper levels, and b) from a practical point of view by proposing future tracks for cancer therapeutics, as cancer is primarily a failure of multicellularity in animals and humans. This approach resorts to the emergent field of knowledge known as philosophy of cancer  [1, 8, 9].

Mathematical and computational oncology has increased the pace of cancer research towards the advancement of personalized therapy. Serving the pressing need to exploit the currently underutilized data, such approaches bring a significant clinical advantage in tailoring the therapy. CHIMERA is a novel system that combines mechanistic modelling and machine learning for personalized chemotherapy and surgery sequencing in breast cancer. It optimizes decision-making in personalized breast cancer therapy by connecting tumor growth behaviour and chemotherapy effects through predictive modelling and learning. We demonstrate the capabilities of CHIMERA in learning simultaneously the tumor growth patterns, across several types of breast cancer, and the pharmacokinetics of a typical breast cancer chemotoxic drug. The learnt functions are subsequently used to predict how to sequence the intervention. We demonstrate the versatility of CHIMERA in simultaneously learning tumor growth and pharmacokinetics under two, typically used, chemotherapy protocol hypotheses.

Oral cancer is most prevalent in low- and middle-income countries where it is associated with late diagnosis. A significant factor for this is the limited access to specialist diagnosis. The use of artificial intelligence for decision making on oral cavity images has the potential to improve cancer management and survival rates. This study forms part of the MeMoSA^® (Mobile Mouth Screening Anywhere) project. In this paper, we extended on our previous deep learning work and focused on the binary image classification of ‘referral’ vs. ‘non-referral’. Transfer learning was applied, with several common pre-trained deep convolutional neural network architectures compared for the task of fine-tuning to a small oral image dataset. Improvements to our previous work were made, with an accuracy of 80.88% achieved and a corresponding sensitivity of 85.71% and specificity of 76.42%.

Breast cancer is the most common cancer among women both in developed and developing countries. Early detection and diagnosis of breast cancer may reduce its mortality and improve the quality of life. Computer-aided detection (CADx) and computer-aided diagnosis (CAD) techniques have shown promise for reducing the burden of human expert reading and improve the accuracy and reproducibility of results. Sparse analysis techniques have produced relevant results for representing and recognizing imaging patterns. In this work we propose a method for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA). In this work we apply dictionary learning to our block based sparse analysis method to classify breast lesions as benign or malignant. The performance of our method in conjunction with LC-KSVD dictionary learning is evaluated using 10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results indicate that the proposed sparse analyses may be a useful component for breast cancer screening applications.

Pancreatic ductal adenocarcinoma (PDAC) patients, who often present with stage III or IV disease, face a dismal prognosis as the 5-year survival rate remains below 10%. Recent studies have revealed that CD4\(^+\) T, CD8\(^+\) T, and/or B cells in specific spatial arrangements relative to intratumoral regions correlate with clinical outcome for patients, but the complex functional states of those immune cell types remain to be incorporated into prognostic biomarker studies. Here, we developed an interpretable machine learning model to analyze the functional relationship between leukocyte-leukocyte or leukocyte-tumor cell spatial proximity, correlated with clinical outcome of 46 therapy-naïve PDAC patients following surgical resection. Using a multiplex immunohistochemistry imaging data set focused on profiling leukocyte functional status, our model identified features that distinguished patients in the fourth quartile from those in the first quartile of survival. The top ranked important features identified by our model, all of which were positive prognostic stratifiers, included CD4 T helper cell frequency among CD45\(^+\) immune cells, frequency of Granzyme B-positivity among CD4 and CD8 T cells, as well as the frequency of PD-1 positivity among CD8 T cells. The spatial proximity of CD4 T- to B cells, and between CD8 T cells and epithelial cells, were also identified as important prognostic features. While spatial proximity features provided valuable prognostic information, the best model required both spatial and phenotypic information about tumor infiltrating leukocytes. Our analysis links the immune microenvironment of PDAC tumors to outcome of patients, thus identifying features associated with more progressive disease.

Availability: https://​github.​com/​eroblin/​NN_​Pseudobs

Here, we present new version 2.1 of tugHall (tumor gene-Hallmark) cancer-cell evolution simulator which is accelerated by clone-based approach. The tool is based on the model connected to the well-known cancer hallmarks with the specific mutational states of tumor-related genes. The hallmark variables depend linearly on the mutational states of tumor-related genes with specific weights. The cell behavior phenotypes are stochastically determined and the phenotypic probabilities are probabilistically interfered by the hallmarks. Approximate Bayesian Computation is applied to find the personalized specific parameters of the model. The variant allele frequencies are used as target data for the analysis. In tugHall 2.1, the Darwinian evolutionary competition amongst different clones is computed due to clone’s death/birth processes. The open-source code is available in the repository www.​github.​com/​tugHall.

Gastric cancer (GC) is the sixth most common worldwide malignancy and the third leading cancer cause of death. Early diagnosis and effective after-surgical monitoring can significantly improve survival rates. Previous studies have revealed several serum biomarkers that are elevated in GC patients, including CEA, CA19-9, and CA72-4. However, sensitivity of these biomarkers is below 30%. Identification of more sensitive and specific to GC markers is critical for individualized therapy of this disease. Here we developed an approach for single-cell transcriptomic data analysis that identifies secretory proteins that are abundantly expressed in GC cells and that could be measurable in the blood. Using early GC scRNA-seq data, we identified 19 secretory proteins significantly overexpressed in GC cells. Notably, 4 proteins (IL32, KLK10, KLK7, OLFM4) have demonstrated more superior sensitivity in comparison to conventional serum markers in previous studies. Moreover, 2 proteins, F12 and CFD, were not previously associated with GC and were not utilized for serum-based testing of other malignancies. Proposed approach has a high potential to be used for serum marker identification in other types of cancers and presented here data could be a source for the development of more sensitive and specific diagnostic panel for early gastric cancer detection and patient post-treatment monitoring.

We study the performance of a variant detection method that is based on a property of tumor phylogenetic tree. Our major contributions are two folds. First, we show the property of tumor phylogenetic tree: the total patterns of mutations are restricted if a multi-regional mutation profile follows a corresponding tumor phylogenetic tree, where a multi-regional mutation profile is a matrix in which predictions of somatic mutations at the corresponding tumor regions are listed. Second, we evaluate the lower and upper bounds of specificity and sensitivity of a phylogeny-based somatic variant detection method under several situations. In the evaluation, we conduct patient-wise variant detection from a noisy multi-regional mutation profile matrix for some genomic positions by utilizing the phylogenetic property; we assume that the phylogenetic information can be extracted from another mutation profile matrix that contains accurate candidates at different genomic positions from the noisy ones. From the evaluation, we find that higher sensitivity is not guaranteed in the phylogeny-based variant detection, but higher specificity is guaranteed for several cases. These findings indicate the tumor phylogeny gives more merit for variant detection based on erroneous long-read sequencers (e.g. Oxford nanopore sequencers) than that based on accurate short-read sequencers (e.g., Illumina sequencer).

Recently developed technologies allow us to view the transcriptome at high resolution while preserving the spatial location of samples. These advances are particularly relevant to cancer research, since clonal theory predicts that nearby cells are likely to belong to the same expanding subclone. Using this evolutionary hypothesis, we develop a statistical procedure which uses a test of local spatial association along with a graph-based approach to infer subclones from spatially resolved RNA-seq data. Our method is robust, scalable, and can be applied to data from any of the existing spatial transcriptomics technologies. On data from spatially resolved RNA-seq of breast cancer tissue, our method infers seven distinct subclones and identifies potential driver genes.

Synonymous mutations inside the coding region which do not alter the amino acid chain are usually considered to have no effect on the protein. However, in recent years it was shown that they may regulate expression levels via various mechanisms, suggesting that they may also play an important role in tumorigenesis.

In the current study, we suggest a pipeline for detecting cancerous synonymous mutations that affect the cancer fitness via regulation of transcription. We demonstrate our approach by reporting cases where cancerous synonymous mutations inside the coding regions of the gene BCL2 are under selection in germinal center B-cell (GCB) lymphoma patients. We provide various lines of evidence that suggest that these mutations contribute to the cancer cell survival via improving the expression levels of the anti-apoptotic BCL2 gene.