Sriganesh Srihari

Targeted Therapies for Triple-Negative Breast Cancer: Combating a Stubborn Disease

Triple-negative breast cancers (TNBCs) constitute a heterogeneous subtype of breast cancers that have a poor clinical outcome. Although no approved targeted therapy is available for TNBCs, molecular-profiling efforts have revealed promising molecular targets, with several candidate compounds having now entered clinical trials for TNBC patients. However, initial results remain modest, thereby highlighting challenges potentially involving intra- and intertumoral heterogeneity and acquisition of therapy resistance. We present a comprehensive review on emerging targeted therapies for treating TNBCs, including the promising approach of immunotherapy and the prognostic value of tumor-infiltrating lymphocytes. We discuss the impact of pathway rewiring in the acquisition of drug resistance, and the prospect of employing combination therapy strategies to overcome challenges towards identifying clinically-viable targeted treatment options for TNBC.

A fine-scale dissection of the DNA double-strand break repair machinery and its implications for breast cancer therapy

DNA-damage response machinery is crucial to maintain the genomic integrity of cells, by enabling effective repair of even highly lethal lesions such as DNA double-strand breaks (DSBs). Defects in specific genes acquired through mutations, copy-number alterations or epigenetic changes can alter the balance of these pathways, triggering cancerous potential in cells. Selective killing of cancer cells by sensitizing them to further DNA damage, especially by induction of DSBs, therefore requires careful modulation of DSB-repair pathways. Here, we review the latest knowledge on the two DSB-repair pathways, homologous recombination and non-homologous end joining in human, describing in detail the functions of their components and the key mechanisms contributing to the repair. Such an in-depth characterization of these pathways enables a more mechanistic understanding of how cells respond to therapies, and suggests molecules and processes that can be explored as potential therapeutic targets. One such avenue that has shown immense promise is via the exploitation of synthetic lethal relationships, for which the BRCA1–PARP1 relationship is particularly notable. Here, we describe how this relationship functions and the manner in which cancer cells acquire therapy resistance by restoring their DSB repair potential.

Systematic tracking of dysregulated modules identifies novel genes in cancer

MOTIVATION Deciphering the modus operandi of dysregulated cellular mechanisms in cancer is critical to implicate novel cancer genes and develop effective anti-cancer therapies. Fundamental to this is meticulous tracking of the behavior of core modules, including complexes and pathways across specific conditions in cancer. RESULTS Here, we performed a straightforward yet systematic identification and comparison of modules across pancreatic normal and cancer tissue conditions by integrating PPI, gene-expression and mutation data. Our analysis revealed interesting change-patterns in gene composition and expression correlation particularly affecting modules responsible for genome stability. Although in most cases these changes indicated impairment of essential functions (e.g., of DNA damage repair), in several other cases we noticed strengthening of modules possibly abetting cancer. Some of these compensatory modules showed switches in transcription regulation and recruitment of tumor inducers (e.g., SOX2 through overexpression). In-depth analysis revealed novel genes in pancreatic cancer, which showed susceptibility to copy-number alterations (e.g., for USP15 in 17 of 67 cases), supported by literature evidence for their involvement in other tumors (e.g., USP15 in glioblastoma). Two of the identified genes, YWHAE and DISC1, further supported the nexus between neural genes and pancreatic carcinogenesis. Extension of this assessment to BRCA1 and BRCA2 breast tumors showed specific differences even across the two sub-types and revealed novel genes involved therein (e.g., TRIM5 and NCOA6). AVAILABILITY Our software CONTOURv1 is available at: http://bioinformatics.org.au/tools-data/.

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organisation of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight their limitations and challenges, in particular at detecting sparse and small or sub‐complexes and discerning overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time‐based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.

Beyond cytokinesis: The emerging roles of CEP55 in tumorigenesis

CEP55 was initially identified as a pivotal component of abscission, the final stage of cytokinesis, serving to regulate the physical separation of two daughter cells. Over the past 10 years, several studies have illuminated additional roles for CEP55 including regulating the PI3K/AKT pathway and midbody fate. Concurrently, CEP55 has been studied in the context of cancers including those of the breast, lung, colon and liver. CEP55 overexpression has been found to significantly correlate with tumor stage, aggressiveness, metastasis and poor prognosis across multiple tumor types and therefore has been included as part of several prognostic ‘gene signatures’ for cancer. Here by discussing in depth the functions of CEP55 across different effector pathways, and also its roles as a biomarker and driver of tumorigenesis, we assemble an exhaustive review, thus commemorating a decade of research on CEP55.

Parameterized Algorithms for Generalized Domination

We study the parameterized complexity of a generalization of Dominating Set problem, namely, the Vector Dominating Set problem. Here, given an undirected graph G= (V,E), with V= {v 1 , ? , v n }, a vector

Breast cancer classification: linking molecular mechanisms to disease prognosis

\vec{l}=(l(v_1),\cdots, l(v_n))

Detecting hubs and quasi cliques in scale-free networks

and an integer parameter k, the goal is to determine whether there exists a subset Dof at most kvertices such that for every vertex v? V? D, at least l(v) of its neighbors are in D. This problem encompasses the well studied problems --- Vertex Cover (when l(v) = d(v) for all v? V, where d(v) is the degree of vertex v) and Dominating Set (when l(v) = 1 for all v? V). While Vertex Cover is known to be fixed parameter tractable, Dominating Set is known to be W[2]-complete. In this paper, we identify vectors based on several measures for which this generalized problem is fixed parameter tractable and W-hard. We also show that the Vector Dominating Set is fixed parameter tractable for graphs of bounded degeneracy and for graphs excluding cycles of length four.

Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach

Accurate subtyping or classification of breast cancer is important for ensuring proper treatment of patients and also for understanding the molecular mechanisms driving this disease. While there have been several gene signatures proposed in the literature to classify breast tumours, these signatures show very low overlaps, different classification performance, and not much relevance to the underlying biology of these tumours. Here we evaluate DNA-damage response (DDR) and cell cycle pathways, which are critical pathways implicated in a considerable proportion of breast tumours, for their usefulness and ability in breast tumour subtyping. We think that subtyping breast tumours based on these two pathways could lead to vital insights into molecular mechanisms driving these tumours. Here, we performed a systematic evaluation of DDR and cell-cycle pathways for subtyping of breast tumours into the five known intrinsic subtypes. Homologous Recombination (HR) pathway showed the best performance in subtyping breast tumours, indicating that HR genes are strongly involved in all breast tumours. Comparisons of pathway based signatures and two standard gene signatures supported the use of known pathways for breast tumour subtyping. Further, the evaluation of these standard gene signatures showed that breast tumour subtyping, prognosis and survival estimation are all closely related. Finally, we constructed an all-inclusive super-signature by combining (union of) all genes and performing a stringent feature selection, and found it to be reasonably accurate and robust in classification as well as prognostic value. Adopting DDR and cell cycle pathways for breast tumour subtyping achieved robust and accurate breast tumour subtyping, and constructing a super-signature which contains feature selected mix of genes from these molecular pathways as well as clinical aspects is valuable in clinical practice.Breast cancer was traditionally perceived as a single disease; however, recent advances in gene expression and genomic profiling have revealed that breast cancer is in fact a collection of diseases exhibiting distinct anatomical features, responses to treatment and survival outcomes. Consequently, a number of schemes have been proposed for subtyping of breast cancer to bring out the biological and clinically relevant characteristics of the subtypes. Although some of these schemes capture underlying molecular differences, others predict variations in response to treatment and survival patterns. However, despite this diversity in the approaches, it is clear that molecular mechanisms drive clinical outcomes, and therefore an effective scheme should integrate molecular as well as clinical parameters to enable deeper understanding of cancer mechanisms and allow better decision making in the clinic. Here, using a large cohort of ∼550 breast tumours from The Cancer Genome Atlas, we systematically evaluate a number of expression-based schemes including at least eight molecular pathways implicated in breast cancer and three prognostic signatures, across a variety of classification scenarios covering molecular characteristics, biomarker status, tumour stages and survival patterns. We observe that a careful combination of these schemes yields better classification results compared with using them individually, thus confirming that molecular mechanisms and clinical outcomes are related and that an effective scheme should therefore integrate both these parameters to enable a deeper understanding of the cancer.

Complex-based analysis of dysregulated cellular processes in cancer

Scale-free networks are believed to closely model most real-world networks. An interesting property of such networks is the existence of so-called hub and community structures. In this paper, we model hubs as high-degree nodes and communities as quasi cliques. We propose a new problem formulation called the ¿-list dominating set and show how this single problem is suited to model both the structures in real-world networks better than traditional problems like vertex cover and clique. Additionally, we provide a fixed-parameter tractable algorithm to this detect these structures and show experimental results on protein-protein interaction networks.