Biology subjects

Wala, J.

Publications and source records attributed to Wala, J..

8 recordsLinked to original sources

bioRxiv2025

Spatially organized lymphocytic microenvironments in high grade primary prostate tumors

The spatial arrangement of immune cells in the tumor microenvironment (TME) varies widely, from dispersed to clustered and tumor excluded to infiltrating. Multiplexed spatial profiling is an effective means of characterizing tumor-infiltrating lymphocytes (TILs) and immune complexes such as tertiary lymphoid structures (TLS) in the TME. However, few approaches have been described for objectively parametrizing patterns of immune organization and assessing their association with biological or clinical variables. This makes it difficult to evaluate whether a set of tumors is relatively immunologically cold or hot. Here we describe an intuitive set of statistical tools (available in the R package, tlsR) for characterizing lymphocyte patterns in the TME of solid cancers. We apply tlsR to primary prostate cancer (PCa), which is often described as immunologically cold. Using a cohort of 29 radical prostatectomy specimens stratified into low Gleason-grade (LGG; n=15) and high Gleason-grades (HGG; n =14) we show that HGG PCa is significantly more infiltrated than LGG PCa with lymphocytes organized into B cell or T cell enriched immune clusters (BICs and TICs). A subset of these ICs have the B and T cell zonation and follicular dendritic cells characteristic of a bona fide TLS. HGGs are also enriched with ICs containing precursor exhausted T cells (Tpex) and proliferating B cells and their tumor compartments harbor granzyme-B+ cytotoxic T cells in contact with cancer cells. Thus, far from being cold, a subset of HGG PCa has features associated with active immune surveillance, a finding with implications for emerging PCa immunotherapies.

cancer biology↗

bioRxiv2025

Immune Spatial Organization Predicts Metastasis Risk in Aggressive Localized Prostate Cancer

Risk stratification in localized prostate cancer (PCa) remains imprecise. Computational pathology has emerged as an attractive option for improving risk stratification, but current approaches either lack interpretability or focus solely on tumor morphology. Here, we identify, validate, and provide interpretability for an immune microenvironment-derived computational pathology biomarker for high-grade PCa. Using digitized hematoxylin and eosin-stained (H&E) slides from two independent cohorts of patients treated with radical prostatectomy (n=490), we found that spatial clustering of immune clusters, but not immune cell abundance, was independently associated with reduced risk of distant metastasis for high-grade disease but not low-grade. Joint analysis of H&E images and RNA sequencing (n=326) revealed that in high-grade disease, high-cluster samples were enriched in CD8+ T cells, activated memory CD4+ T cells, and Tregs, as well as clonal T cell populations, overall suggestive of an underlying T cell-mediated antitumor response. No such enrichment was found in low-grade tumors. These findings establish immune spatial architecture as a novel, interpretable computational pathology biomarker and provide insight into the immune landscape of high-grade PCa.

bioinformatics↗

bioRxiv2024

Integrating spatial profiles and cancer genomics to identify immune-infiltrated mismatch repair proficient colorectal cancers

Predicting the progression of solid cancers based solely on genetics is challenging due to the influence of the tumor microenvironment (TME). For colorectal cancer (CRC), tumors deficient in mismatch repair (dMMR) are more immune infiltrated than mismatch repair proficient (pMMR) tumors and have better prognosis following resection. Here we quantify features of the CRC TME by combining spatial profiling with genetic analysis and release our findings via a spatially enhanced version of cBioPortal that facilitates multi-modal data exploration and analysis. We find that [~]20% of pMMR tumors exhibit similar levels of T cell infiltration as dMMR tumors and that this is associated with better survival but not any specific somatic mutation. These T cell-infiltrated pMMR (tipMMR) tumors contain abundant cells expressing PD1 and PDL1 as well as T regulatory cells, consistent with a suppressed immune response. Thus, like dMMR CRC, tipMMR CRC may benefit from immune checkpoint inhibitor therapy. SIGNIFICANCEpMMR tumors with high T cell infiltration and active immunosuppression are identifiable with a mid-plex imaging assay whose clinical deployment might double the number of treatment-naive CRCs eligible for ICIs. Moreover, the low tumor mutational burden in tipMMR CRC shows that MMR status is not the only factor promoting immune infiltration.

cancer biology↗

bioRxiv2024

Recurrent breakpoints in the BRD4 locus reduce toxicity associated with gene amplification

Structural variants (SVs) represent a mechanism by which cancer cells activate oncogenes or disrupt the function of genes with tumor suppressor roles. A recent study by the PCAWG Consortium investigated structural variants in 30 tumor types, identifying focal rearrangements in the oncogenic BRD4 gene in ovarian, endometrial and breast cancers. These rearrangements resulted in decreased BRD4 expression despite increased copy number, suggesting a novel mechanism to finetune gene over-expression. In this study, we show that focal deletions of BRD4 disrupt genomic regulatory regions and impact gene isoform expression in breast and ovarian tumors when compared to their expression across normal tissues. To determine the functional impact of these concomitant amplification and focal deletion events, we first leveraged open-reading-frame (ORF) screen data from 16 cancer cell lines, where we observed that overexpression of BRD4-long and BRD4-short isoforms is toxic for cancer cell growth. We confirmed these results in OVSAHO ovarian cancer cells, where overexpression of both isoforms significantly reduced tumor growth. Next, we mimicked the focal deletions occurring in BRD4 regulatory regions by CRISPR-Cas9 technology, and observed that their depletion functionally ablates tumor cell growth. We finally show that these focal deletions rescue ovarian carcinoma cells from the toxicity effects associated with gene overexpression, suggesting that global BRD4 gene expression levels must be fine tuned to ensure proper cancer cell proliferation. Our study provides experimental evidence for BRD4 deletions constituting the first example of a driver SV alteration reducing toxicity in cancer, therefore expanding the landscape of cancer progression mechanisms.

cancer biology↗

bioRxiv2023

Detecting significantly recurrent genomic connections from simple and complex rearrangements in the cancer genome

The detection of somatic genetic alterations that recur across cancer genomes more than expected by chance has been a major goal of cancer genomics, as these alterations are enriched for "driver" events that promote cancer. Multiple methods have been developed to detect driver point mutations and copy-number variants, but methods to detect driver rearrangements have largely not been pursued. Unlike point mutations and copy-number alterations, which can be assigned to a single genomic locus, rearrangements connect two distant genomic loci, and possibly more in the case of complex or clustered events. Here, we explore genomic features that predict the rate at which any pair of loci will be connected by rearrangements and describe two methods to detect rearrangements that recur more often than this background rate. The first, SVSig-2D, detects pairs of loci that are directly connected by a single rearrangement; the second, SVSig-2Dc also detects loci that are recurrently connected indirectly through two or more rearrangements. When applied to a pan-cancer dataset of over 2,500 cancers, these methods identified 80 significantly recurrent simple rearrangements and 29 complex rearrangements, including both known and novel events. Intriguingly, though both recurrent simple and complex rearrangements tended to be tissue-specific, this was less true for the complex events. The detection of recurrent rearrangements with methods such as these will be an essential component of cancer genomics in the whole-genome sequencing era.

genomics↗

bioRxiv2019

A robust benchmark for germline structural variant detection

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls [≥]50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.

genomics↗

bioRxiv2017

The whole-genome panorama of cancer drivers

The advance of personalized cancer medicine requires the accurate identification of the mutations driving each patients tumor. However, to date, we have only been able to obtain partial insights into the contribution of genomic events to tumor development. Here, we design a comprehensive approach to identify the driver mutations in each patients tumor and obtain a whole-genome panorama of driver events across more than 2,500 tumors from 37 types of cancer. This panorama includes coding and non-coding point mutations, copy number alterations and other genomic rearrangements of somatic origin, and potentially predisposing germline variants. We demonstrate that genomic events are at the root of virtually all tumors, with each carrying on average 4.6 driver events. Most individual tumors harbor a unique combination of drivers, and we uncover the most frequent co-occurring driver events. Half of all cancer genes are affected by several types of driver mutations. In summary, the panorama described here provides answers to fundamental questions in cancer genomics and bridges the gap between cancer genomics and personalized cancer medicine.

cancer biology↗

bioRxiv2017

Genome-wide detection of structural variants and indels by local assembly

Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at-scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABAs performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs, and substantially improved detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (< 1,000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types, and found that templated-sequence insertions occur in ~4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized SVs.

genomics↗