Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU

Polycomb safeguards imaginal disc specification through control of the Vestigial-Scalloped complex

A fundamental goal of developmental biology is to understand how cell and tissue fates are specified. The imaginal discs of Drosophila are excellent model systems for addressing this paradigm as their fate can be redirected when discs regenerate after injury or when key selector genes are misregulated. Here, we show that when Polycomb expression is reduced, the wing selector gene vestigial is ectopically activated. This leads to the inappropriate formation of the Vestigial-Scalloped complex, which forces the eye to transform into a wing. We further demonstrate that disrupting this complex does not simply block wing formation or restore eye development. Instead, immunohistochemistry and high-throughput genomic analysis show that the eye-antennal disc unexpectedly undergoes hyperplastic growth with multiple domains being organized into other imaginal discs and tissues. These findings provide insight into the complex developmental landscape that tissues must navigate before adopting their final fate.

Co-existence of 2 clinically significant variants causing disorders of somatic mosaicism

Purpose

Disorders of somatic mosaicism (DoSM) are a heterogeneous group of conditions caused by postzygotic variants in genes within the PI3K/AKT/mTOR and RAS/MAPK signaling pathway. The co-existence of 2 activating variants in this disease group is extremely rare.

Methods

A deep sequencing next-generation sequencing assay for the molecular diagnosis of DoSM was run on 936 individuals with DoSM.

Results

A single pathogenic or likely pathogenic (P/LP) variant was identified in 584 of 617 (94.8%) positive cases; 33 of 617 (5.2%) cases carried 2 P/LP variants. Of these 33 cases, 22 carried 2 P/LP variants in the same gene, including 8 associated with a loss-of-function disease mechanism and 14 associated with a gain-of-function disease mechanism. Eleven cases had P/LP variants in 2 different genes, including PIKC3A variants in 7 cases and 4 cases with 2 P/LP variants in non-PIK3CA genes.

Conclusion

To our knowledge, this is the largest cohort with the co-existence of 2 P/LP somatic variants causing DoSM. The study of the co-existence of 2 clinically significant variants in DoSM requires unique considerations regarding variant allelic fractions, the combination of variants, affected tissue types, and the severity of the disease. Investigations into this unique cohort may further our understanding of the disease mechanism and potential therapeutic options.

The underexplored links between cancer and the internal body climate: Implications for cancer prevention and treatment

In order to effectively manage and cure cancer we should move beyond the general view of cancer as a random process of genetic alterations leading to uncontrolled cell proliferation or simply a predictable evolutionary process involving selection for traits that increase cell fitness. In our view, cancer is a systemic disease that involves multiple interactions not only among cells within tumors or between tumors and surrounding tissues but also with the entire organism and its internal "milieu". We define the internal body climate as an emergent property resulting from spatial and temporal interactions among internal components themselves and with the external environment. The body climate itself can either prevent, promote or support cancer initiation and progression (top-down effect; i.e., body climate-induced effects on cancer), as well as be perturbed by cancer (bottom-up effect; i.e., cancer-induced body climate changes) to further favor cancer progression and spread. This positive feedback loop can move the system towards a "cancerized" organism and ultimately results in its demise. In our view, cancer not only affects the entire system; it is a reflection of an imbalance of the entire system. This model provides an integrated framework to study all aspects of cancer as a systemic disease, and also highlights unexplored links that can be altered to both prevent body climate changes that favor cancer initiation, progression and dissemination as well as manipulate or restore the body internal climate to hinder the success of cancer inception, progression and metastasis or improve therapy outcomes. To do so, we need to (i) identify cancer-relevant factors that affect specific climate components, (ii) develop 'body climate biomarkers', (iii) define 'body climate scores', and (iv) develop strategies to prevent climate changes, stop or slow the changes, or even revert the changes (climate restoration).

Identification of risk variants related to malignant tumors in children with birth defects by whole genome sequencing

Background

Children with birth defects (BD) are more likely to develop cancer and the increased risk of cancer persists into adulthood. Prior population-based assessments have demonstrated that even non-chromosomal BDs are associated with at least two-fold increase of cancer risk. Identification of variants that are associated with malignant tumor in BD patients without chromosomal anomalies may improve our understanding of the underlying molecular mechanisms and provide clues for early cancer detection in children with BD.

Methods

In this study, whole genome sequencing (WGS) data of blood-derived DNA for 1653 individuals without chromosomal anomalies were acquired from the Kids First Data Resource Center (DRC), including 541 BD probands with at least one type of malignant tumors, 767 BD probands without malignant tumor, and 345 healthy family members who are the parents or siblings of the probands. Recurrent variants exclusively seen in cancer patients were selected and mapped to their corresponding genomic regions. The targeted genes/non-coding RNAs were further reduced using random forest and forward feature selection (ffs) models.

Results

The filtered genes/non-coding RNAs, including variants in non-coding areas, showed enrichment in cancer-related pathways. To further support the validity of these variants, blood WGS data of additional 40 independent BD probands, including 25 patients with at least one type of cancers from unrelated projects, were acquired. The counts of variants of interest identified in the Kid First data showed clear deviation in the validation dataset between BD patients with cancer and without cancer. Furthermore, a deep learning model was built to assess the predictive abilities in the 40 patients using variants of interest identified in the Kids First cohort as feature vectors. The accuracies are ~ 75%, with the noteworthy observation that variants mapped to non-coding regions provided the highest accuracy (31 out of 40 patients were labeled correctly).

Conclusion

We present for the first time a panorama of genetic variants that are associated with cancers in non-chromosomal BD patients, implying that our approach may potentially serve for the early detection of malignant tumors in patients with BD.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Identifying modules of cooperating cancer drivers

Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co-occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.