Biology subjects

Haradhvala, N. J.

Publications and source records attributed to Haradhvala, N. J..

8 recordsLinked to original sources

bioRxiv2025

A Diffusion-Based Autoencoder for Learning Patient-Level Representations from Single-Cell Data

Single-cell RNA sequencing (scRNA-seq) offers insights into cellular heterogeneity and tissue composition, yet leveraging this data for patient-level clinical predictions remains challenging due to the set-structured nature of single-cell data, as well as the scarcity of labeled samples. To address these challenges, we introduce scSet, a diffusion-based autoencoder that learns patient-level representations from sets of single-cell transcriptomes. Our method uses a transformer-based encoder to process variably sized and unordered cell inputs, coupled with a conditional diffusion decoder for self-supervised learning on unlabeled data. By pre-training on large-scale unlabeled datasets, scSet generates robust patient representations that can be fine-tuned for downstream clinical prediction tasks. We demonstrate the effectiveness of scSet patient embeddings for clinical prediction across multiple real-world datasets, where they outperform existing patient representations, even with limited labeled data. This work represents an important step toward bridging the gap between single-cell resolution and patient-level insights. Code is available at https://github.com/clinicalml/scset.

bioinformatics↗

bioRxiv2023

Systematic characterization of therapeutic vulnerabilities in Multiple Myeloma with Amp1q reveals increased sensitivity to the combination of MCL1 and PI3K inhibitors

The development of targeted therapy for patients with Multiple Myeloma (MM) is hampered by the low frequency of actionable genetic abnormalities. Gain or amplification of chr1q (Amp1q) is the most frequent arm-level copy number gain in patients with MM, and it is associated with higher risk of progression and death despite recent advances in therapeutics. Thus, developing targeted therapy for patients with MM and Amp1q stands to benefit a large portion of patients in need of more effective management. Here, we employed large-scale dependency screens and drug screens to systematically characterize the therapeutic vulnerabilities of MM with Amp1q and showed increased sensitivity to the combination of MCL1 and PI3K inhibitors. Using single-cell RNA sequencing, we compared subclones with and without Amp1q within the same patient tumors and showed that Amp1q is associated with higher levels of MCL1 and the PI3K pathway. Furthermore, by isolating isogenic clones with different copy number for part of the chr1q arm, we showed increased sensitivity to MCL1 and PI3K inhibitors with arm-level gain. Lastly, we demonstrated synergy between MCL1 and PI3K inhibitors and dissected their mechanism of action in MM with Amp1q.

cancer biology↗

bioRxiv2020

Efficient design of maximally active and specific nucleic acid diagnostics for thousands of viruses

Diagnostics, particularly for rapidly evolving viruses, stand to benefit from a principled, measurement-driven design that harnesses machine learning and vast genomic data--yet the capability for such design has not been previously built. Here, we develop and extensively validate an approach to designing viral diagnostics that applies a learned model within a combinatorial optimization framework. Concentrating on CRISPR-based diagnostics, we screen a library of 19,209 diagnostic-target pairs and train a deep neural network that predicts, from RNA sequence alone, diagnostic signal better than contemporary techniques. Our model then makes it possible to design assays that are maximally sensitive over the spectrum of a viruss genomic variation. We introduce ADAPT (https://adapt.guide), a system for fully-automated design, and use ADAPT to design optimal diagnostics for the 1,933 vertebrate-infecting viral species within 2 hours for most species and 24 hours for all but 3. We experimentally show ADAPTs designs are sensitive and specific down to the lineage level, including against viruses that pose challenges involving genomic variation and specificity. ADAPTs designs exhibit significantly higher fluorescence and permit lower limits of detection, across a viruss entire variation, than the outputs of standard design techniques. Our model-based optimization strategy has applications broadly to viral nucleic acid diagnostics and other sequence-based technologies, and, paired with clinical validation, could enable a critically-needed, proactive resource of assays for surveilling and responding to pathogens.

genomics↗

bioRxiv2019

Passenger Hotspot Mutations in Cancer

Hotspots, or mutations that recur at the same genomic site across multiple tumors, have been conventionally interpreted as strong universal evidence of somatic positive selection, unequivocally pinpointing genes driving tumorigenesis. Here, we demonstrate that this convention is falsely premised on an inaccurate statistical model of background mutagenesis. Many hotspots are in fact passenger events, recurring at sites that are simply inherently more mutable rather than under positive selection, which current background models do not account for. We thus detail a log-normal-Poisson (LNP) background model that accounts for variation in site-specific mutability in a manner consistent with models of mutagenesis, use this model to show that the tendency to generate passenger hotspots pervades all common mutational processes, and apply it to a ~10, 000 patient cohort from The Cancer Genome Atlas to nominate driver hotspots with far fewer false positives compared to conventional methods. As the biomedical community faces critical decisions in prioritizing putative driver mutations for deep experimental characterization to assess therapeutic potential, we offer our findings as a guide to avoid wasting valuable scientific resources on passenger hotspots.

genomics↗

bioRxiv2019

Tumor mutational landscape is a record of the pre-malignant state

Chromatin structure has a major influence on the cell-specific density of somatic mutations along the cancer genome. Here, we present a pan-cancer study in which we searched for the putative cancer cell-of-origin of 2,550 whole genomes, representing 32 cancer types by matching their mutational landscape to the regional patterns of chromatin modifications ascertained in 104 normal tissue types. We found that, in almost all cancer types, the cell-of-origin can be predicted solely from their DNA sequences. Our analysis validated the hypothesis that high-grade serous ovarian cancer originates in the fallopian tube and identified distinct origins of breast cancer subtypes. We also demonstrated that the technique is equally capable of identifying the cell-of-origin for a series of 2,044 metastatic samples from 22 of the tumor types available as primaries. Moreover, cancer drivers, whether inherited or acquired, reside in active chromatin regions in the respective cell-of-origin. Taken together, our findings highlight that many somatic mutations accumulate while the chromatin structure of the cell-of-origin is maintained and that this historical record, captured in the DNA, can be used to identify the often elusive cancer cell-of-origin.

genomics↗

bioRxiv2018

A post-transcriptional program of chemoresistance by AU-rich elements/TTP in cancer quiescence

BackgroundQuiescence (G0) is a transient, cell cycle-arrested state. By entering G0, cancer cells survive unfavorable conditions such as chemotherapy and cause relapse. While G0 cells have been studied at the transcriptome level, how post-transcriptional regulation contributes to their chemoresistance remains unknown.\n\nResultsWe induced chemoresistant and quiescent (G0) leukemic cells by serum-starvation or chemotherapy treatment. To study post-transcriptional regulation in G0 leukemic cells, we systematically analyzed their transcriptome, translatome, and proteome. We find that our resistant G0 cells recapitulate gene expression profiles of in vivo chemoresistant leukemic and G0 models. In G0 cells, canonical translation initiation is inhibited; yet we find that inflammatory genes are highly translated, indicating alternative post-transcriptional regulation. Importantly, AU-rich elements (AREs) are significantly enriched in the up-regulated G0 translatome and transcriptome. Mechanistically, we find the stress-responsive p38 MAPK-MK2 signaling pathway stabilizes ARE mRNAs by phosphorylation and inactivation of mRNA decay factor, tristetraprolin (TTP) in G0. This permits expression of ARE-bearing TNF and DUSP1 that promote chemoresistance. Conversely, inhibition of TTP phophorylation by p38 MAPK inhibitors and non-phosphorylatable TTP mutant decreases ARE mRNAs and sensitizes leukemic cells to chemotherapy. Furthermore, co-inhibiting p38 MAPK and TNF--prior to or along with chemotherapy--substantially reduced chemoresistance in primary leukemic cells ex vivo and in vivo.\n\nConclusionsThese studies uncover post-transcriptional regulation underlying chemoresistance in leukemia. Our data reveal the p38 MAPK-MK2-TTP axis as a key regulator of expression of ARE bearing mRNAs that promote chemoresistance. By disrupting this pathway, we developed an effective combination therapy against chemosurvival.

cancer biology↗

bioRxiv2018

The Repertoire of Mutational Signatures in Human Cancer

Somatic mutations in cancer genomes are caused by multiple mutational processes each of which generates a characteristic mutational signature. Using 84,729,690 somatic mutations from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer types we characterised 49 single base substitution, 11 doublet base substitution, four clustered base substitution, and 17 small insertion and deletion mutational signatures. The substantial dataset size compared to previous analyses enabled discovery of new signatures, separation of overlapping signatures and decomposition of signatures into components that may represent associated, but distinct, DNA damage, repair and/or replication mechanisms. Estimation of the contribution of each signature to the mutational catalogues of individual cancer genomes revealed associations with exogenous and endogenous exposures and defective DNA maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes contributing to the development of human cancer including a comprehensive reference set of mutational signatures in human cancer.

cancer biology↗

bioRxiv2017

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗