Biology subjects

Tallman, D.

Publications and source records attributed to Tallman, D..

3 recordsLinked to original sources

bioRxiv2026

CNSigs: An R Package for the Identification of Copy Number Mutational Signatures

Copy number aberrations (CNAs) are gains and losses of large genomic segments present across most cancer types and are a hallmark of cancer genomic alterations. However, the processes underlying CNAs and characteristic patterns of CNAs are poorly understood. Bioinformatic advances have identified underlying single nucleotide variant mutational signatures resulting from distinct mutational processes, yet development of algorithms able to uncover similar signatures for CNAs remains less advanced. Using segmented data files from DNA sequencing, six copy number features are extracted for signature determination: segment size, breakpoints, copy number oscillation, changepoint size, copy number, and breakpoints per chromosome arm, along with ploidy. Mixed model approaches and non-negative matrix factorization are utilized to derive CNA signatures across cancer types. The full methodology was packaged in a publicly available, robust R package, CNSigs. To verify reproducibility, we derived five signatures from two independent breast cancer datasets (total n>3000), demonstrating high accuracy (average cosine similarity = 0.89). Pan-cancer application of CNSigs in TCGA resulted in derivation of 13 pan-cancer signatures which were significantly associated with disease-specific survival. Benchmarking CNSigs to two other CNA signature approaches within TCGA demonstrated non-overlapping signatures and favorable compute speed for CNSigs. We evaluated n=24 pairs of tumor and circulating tumor DNA (ctDNA) that demonstrated that CNSigs are detectable and reproducible via ctDNA, with significant association of CNSig11 with metastatic triple-negative breast cancer progression-free survival specifically for taxane chemotherapy. CNSigs association with immunophenotype was evaluated in low-grade glioma and CNSig3 was found to be highly prognostic yet complementary to immune features. The CNSigs allows researchers to easily analyze their own samples to derive copy number signatures and evaluate clinical associations. We demonstrate its potential application in ctDNA and association with treatment response. The development of this package allows further investigation of underlying processes that may be responsible for CNA fingerprints.

bioinformatics↗

bioRxiv2021

MKLP2 functions in early mitosis to ensure proper chromosome congression

Mitotic kinesin-like protein 2 (MKLP2) is a motor protein with a well-established function in promoting cytokinesis. However, our results with siRNAs targeting MKLP2 and small molecule inhibitors of MKLP2 (MKLP2i) suggested a function earlier in mitosis, prior to anaphase. In this study we provide direct evidence that MKLP2 facilitates chromosome congression in prometaphase. We employed live imaging to observe HeLa cells with fluorescently tagged histones treated with MKLP2i and discovered a pronounced chromosome congression defect. We show that MKLP2 is essential for pole-based error correction as inhibited cells had a significant increase in unstable kinetochore-microtubule attachments due to impaired error correction of syntelic attachments. We propose that MKLP2 mediates kinetochore microtubule attachment stability by regulating Aurora Kinase activity and a downstream target, pHEC1 (Ser 55). Lastly, we show that MKLP2 inhibition results in aneuploidy, confirming that MKLP2 safeguards cells against chromosomal instability. SummarySchrock et al. demonstrate that the mitotic kinesin, MKLP2, is required for congression of chromosomes located near the spindle poles. They show that MKLP2 inhibition causes elevated active Aurora Kinase A, unstable microtubule kinetochore attachments, and impaired syntelic error correction.

cell biology↗

bioRxiv2019

Machine learning predicts rapid relapse of triple negative breast cancer

PurposeMetastatic relapse of triple-negative breast cancer (TNBC) within 2 years of diagnosis is associated with particularly aggressive disease and a distinct clinical course relative to TNBCs that relapse beyond 2 years. We hypothesized that rapid relapse TNBCs (rrTNBC; metastatic relapse or death <2 years) reflect unique genomic features relative to late relapse (lrTNBC; >2 years).\n\nPatients and MethodsWe identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBc, lrTNBC, or no relapse (nrTNBC: no relapse/death with at least 5 years follow-up). We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated published gene expression and immune signatures.\n\nResultsPatients with rrTNBC were higher stage at diagnosis (Chi-square p<0.0001) while lrTNBC were more likely to be non-basal PAM50 subtype (Chi-square p=0.03). Among 125 expression signatures, five immune signatures were significantly higher in nrTNBCs while lrTNBC were enriched for eight estrogen/luminal signatures (all FDR p<0.05). There was no significant difference in tumor mutation burden or percent genome altered across the groups. Among mutations, only TP53 mutations were significantly more frequent in rrTNBC compared to lrTNBC (Fisher exact FDR p=0.009). To develop an optimal classifier, we used 77 significant clinical and omic features to evaluate six modeling approaches encompassing simple, machine learning, and artificial neural network (ANN). Support vector machine outperformed other models with average receiver-operator characteristic area under curve >0.75.\n\nConclusionsWe provide a new approach to define TNBCs based on timing of relapse. We identify distinct clinical and genomic features that can be incorporated into machine learning models to predict rapid relapse of TNBC.

cancer biology↗