Biology subjects

Malla, S. B.

Publications and source records attributed to Malla, S. B..

4 recordsLinked to original sources

bioRxiv2024

Viral mimicry redirects immunosuppressed colorectal tumour landscapes towards a proinflammatory and CMS1-like regenerative state

In colorectal cancer (CRC), tumours classifier as consensus molecular subtype 4 (CMS4) have the worst prognosis and derive negligible benefit from chemotherapy. We previously described how repressed interferon-related signalling is associated with increased relapse in CMS4 tumours. Although the viral mimetic poly(I:C) can reduce liver metastasis in vivo, the initial phenotypic changes that underpin its anti-metastatic response remain poorly described, particularly in the immunosuppressed CMS4 tumour microenvironment. Here we characterise lineage-specific anti-metastatic responses induced by poly(I:C), including acute macrophage polarisation and a novel CMS1-like regenerative stem cell state, which drive pro-inflammatory microenvironmental changes in CRC. These insights enabled the development of tractable biomarkers that identify an "immune-warm" patient subset most likely to respond to poly(I:C), enriched for mismatch-repair proficient (pMMR), anti-inflammatory macrophages and CMS4-like features. The viral mimetic poly(I:C) offers a tailored treatment option for CMS4 tumours, by reprogramming stem cell states and activation of an innate-adaptive anti-metastatic response.

cancer biology↗

bioRxiv2024

Mitochondrial metabolism is a key determinant of chemotherapy sensitivity in Colorectal Cancer

Therapy resistance is attributed to over 80% of cancer deaths per year emphasizing the urgent need to overcome this challenge for improved patient outcomes. Despite its widespread use in colorectal cancer (CRC) treatment, resistance to 5-fluorouracil (5FU) remains poorly understood. Here, we investigate the transcriptional responses of CRC cells to 5FU treatment, revealing significant metabolic reprogramming towards heightened mitochondrial activity. Utilizing CRC models, we demonstrate sustained enhancement of mitochondrial biogenesis and function following 5FU treatment, leading to resistance in both in vitro and in vivo settings. Furthermore, we show that targeting mitochondrial metabolism, specifically by inhibiting Complex I (CI), sensitizes CRC cells to 5FU, resulting in delayed tumour growth and prolonged survival in preclinical models. Additionally, our analysis of patient data suggests that oxidative metabolism signatures may predict responses to 5FU-based chemotherapy. These findings shed light on mechanisms underlying 5FU resistance and propose a rational strategy for combination therapy in CRC, emphasizing the potential clinical benefit of targeting mitochondrial metabolism to overcome resistance and enhance patient outcomes.

cancer biology↗

bioRxiv2024

Library size can undermine accurate molecular and phenotypic subtyping in spatial transcriptomics data.

In an era where transcriptomics-based subtyping, phenotyping and mechanistic understanding is increasingly being driven by state-of-the-art spatially resolved transcriptomic (ST) technologies, it is imperative that researchers, journals, and funders do all they can to ensure that as a community we are interpreting these exciting data as accurately as possible with awareness of their limitations. In this short report, we highlight one potential bias in ST data that could undermine accurate interpretation of transcriptional signatures, providing the field with an opportunity to identify and avoid this issue prior to release of new mechanistic findings. This issue is particularly relevant for platforms that produce some of the most granular and high-resolution spatial information at single cell (and sub-cellular) resolution, with the compromise of a reduced transcriptome panel of genes (Figure 1A). O_FIG O_LINKSMALLFIG WIDTH=141 HEIGHT=200 SRC="FIGDIR/small/602370v1_fig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1219fb3org.highwire.dtl.DTLVardef@7bd4a4org.highwire.dtl.DTLVardef@1c55c42org.highwire.dtl.DTLVardef@2bf924_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Visualisation of the iCMS3 up signature A: Schematic overview of enrichment using a full gene panel versus a reduced panel. B: Overlap between the genes represented on the CosMx and Xenium gene panels. C: Correlation between the single sample scores of the full iCMS3 up signature (74 genes) and the 15 genes from the signature represented on the CosMx platform. Two samples are highlighted which have a similar iCMS3 up enrichment for the full signature (CRC-JSC-S06: 4535.746; SMC16: 4265.263) but extreme enrichments for the signature composed only of the genes present on the CosMx array (CRC-JSC-S06: 301.0396; SMC16: 11430.593). Median (4047.994) shown by red line. D: Visualisation of the rank of each sample across for the full (CRC-JSC-S06: position 25872/44458; SMC16: position 23907/44458) and CosMx (CRC-JSC-S06: position 513/44458; SMC16: position 44241/44458) signature enrichment. E: Heatmap showing the relative enrichment of each gene with the iCMS3 up signature for each sample, with the genes present on the CosMx array in red. F: Subset of samples (n=628) +/-1% of the median (4007.514 - 4088.474), with the top 100 and bottom 100 samples for CosMx enrichment in red. G: Heatmap of the top 100 and bottom 100 samples (shown in red in F) for CosMx enrichment with a full iCMS3 up enrichment around the median. The samples are arranged by the rank of each sample for CosMx signature enrichment, with the sum of the genes within the iCMS3 up signature present on the array (CosMx [n=16]) and those not present on the array (Non CosMx [n=58]) overlaid as barplots. C_FIG

cancer biology↗

bioRxiv2024

Evaluation of Gene Set Enrichment Analysis (GSEA) tools highlights the value of single sample approaches over pairwise for robust biological discovery.

BackgroundGene set enrichment analysis (GSEA) tools can be used to identify biological insights from transcriptional datasets and have become an integral analysis within gene expression-based cancer studies. Over the years, additional methods of GSEA-based tools have been developed, providing the field with an ever-expanding range of options to choose from. Although several studies have compared the statistical performance of these tools, the downstream biological implications that arise when choosing between the range of pairwise or single sample forms of GSEA methods remain understudied. MethodsIn this study, we compare the statistical and biological interpretation of results obtained when using a variety of pre-ranking methods and options for pairwise GSEA and fast GSEA (fGSEA), alongside single sample GSEA (ssGSEA) and gene set variation analysis (GSVA). These analyses are applied to a well-established cohort of n=215 colon tumour samples, using the clinical feature of cancer recurrence status, non-relapse (NR) and relapse (R), as an initial exemplar, in conjunction with the Molecular Signatures Database "Hallmark" gene sets. ResultsDespite minor fluctuations in statistical performance, pairwise analysis revealed remarkably similar results when deployed using a range of gene pre-ranking methods or across a range of choices of GSEA versus fGSEA, with the same well-established prognostic signatures being consistently returned as significantly associated with relapse status. In contrast, when the same statistically significant signatures, such as Interferon Gamma Response, were assessed using ssGSEA and GSVA approaches, there was a complete absence of biological distinction between these groups (NR and R). ConclusionsData presented here highlights how pairwise methods can overgeneralise biological enrichment within a group, assigning strong statistical significance to gene sets that may be inadvertently interpreted as equating to distinct biology. Importantly, single sample approaches allow users to clearly visualise and interpret statistical significance alongside biological distinction between samples within groups-of-interest; thus, providing a more robust and reliable basis for discovery research.

cancer biology↗