Biology subjects

Cisneros, L.

Publications and source records attributed to Cisneros, L..

8 recordsLinked to original sources

bioRxiv2025

Tumor Landscape Analysis: An Ecologically Informed Framework to Understand Tumor Microenvironments

Tumor microenvironments (TMEs) are spatially complex and dynamic systems shaped by evolutionary pressures, tissue architecture, and cellular interactions. To capture this complexity, we developed the Tumor Landscape Analysis (TLA) pipeline, a computational framework applying principles from landscape ecology and spatial statistics to quantitatively characterize tumor spatial heterogeneity. TLA integrates spatially resolved pathology data, including whole-cell, point-based, and region-level formats, and computes multiscale metrics to assess cell distributions, neighborhood relationships, and tissue-level organization. The framework leverages ecological indices such as the Morisita-Horn index, Ripleys H function, and Shannon diversity to quantify intercellular proximity, spatial clustering, and cellular diversity. It also uses the concept of local microenvironments (LMEs), data-driven ecological niches defined by local cell-type abundance and spatial uniformity, enabling unsupervised and reproducible classification of tumor regions. The use of fragmentation metrics, including patch density, shape complexity, and interspersion, provide further insight into spatial disorganization and emergent tissue architecture. TLA is agnostic to imaging modality and biological context, supporting broad applicability across tumor types and sample formats. By translating complex tissue architectures into interpretable spatial metrics, the pipeline enables integrative analyses that link spatial ecology to clinical and molecular phenotypes. This approach facilitates a deeper understanding of how spatial features contribute to tumor progression, therapeutic resistance, and clinical outcomes, offering new opportunities for precision oncology rooted in spatial systems biology.

cancer biology↗

bioRxiv2024

Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer

Immune system control is a major hurdle that cancer evolution must circumvent. The relative timing and evolutionary dynamics of subclones that have escaped immune control remain incompletely characterized, and how immune-mediated selection shapes the epigenome has received little attention. Here, we infer the genome- and epigenome-driven evolutionary dynamics of tumour-immune coevolution within primary colorectal cancers (CRCs). We utilise our existing CRC multi-region multi-omic dataset that we supplement with high-resolution spatially-resolved neoantigen sequencing data and highly multiplexed imaging of the tumour microenvironment (TME). Analysis of somatic chromatin accessibility alterations (SCAAs) reveals frequent somatic loss of accessibility at antigen presenting genes, and that SCAAs contribute to silencing of neoantigens. We observe that strong immune escape and exclusion occur at the outset of CRC formation, and that within tumours, including at the microscopic level of individual tumour glands, additional immune escape alterations have negligible consequences for the immunophenotype of cancer cells. Further minor immuno-editing occurs during local invasion and is associated with TME reorganisation, but that evolutionary bottleneck is relatively weak. Collectively, we show that immune evasion in CRC follows a "Big Bang" evolutionary pattern, whereby genetic, epigenetic and TME-driven immune evasion acquired by the time of transformation defines subsequent cancer-immune evolution.

cancer biology↗

bioRxiv2023

Testing Adaptive Therapy Protocols using Gemcitabine and Capecitabine on a Mouse Model of Endocrine-Resistant Breast Cancer

Highly effective cancer therapies often face limitations due to acquired resistance and toxicity. Adaptive therapy, an ecologically inspired approach, seeks to control therapeutic resistance and minimize toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life over maximum cell kill. In preparation for a clinical trial in breast cancer, we used large populations of MCF7 cells to rapidly generate endocrine-resistance breast cancer cell line. We then mimicked second line therapy in ER+ breast cancers by treating the endocrine-resistant MCF7 cells in a mouse xenograft model to test adaptive therapy with capecitabine, gemcitabine, or the combination of those two drugs. Dose-modulation adaptive therapy with capecitabine alone increased survival time relative to MTD, but not statistically significant (HR: 0.22, 95% CI 0.043-1.1 P = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI: 0.024 - 0.55, P = 0.007) and intermittent adaptive therapies significantly increased survival time compared to high dose combination therapy (HR = 0.07, 95% CI: 0.013 - 0.42; P = 0.003). Overall, survival time increased with reduced dose for both single drugs (P < 0.01) and combined drugs (P < 0.001). Adaptive therapy protocols resulted in tumors with lower proportions of proliferating cells (P = 0.0026) and more apoptotic cells (P = 0.045). The results show that Adaptive therapy outperforms high-dose therapy in controlling endocrine-resistant breast cancer, favoring slower-growing tumors, and showing promise in two-drug alternating regimens.

cancer biology↗

bioRxiv2023

In Silico Investigations of Adaptive Therapy Using a Single Cytotoxic or a Single Cytostatic Drug

Adaptive therapy, as per the dose modulation, dose-skipping, or intermittent treatment protocol works well for treatment using a single cytotoxic drug, under a wide range of scenarios and parameter settings. In contrast, adaptive therapy works well only under a limited number of scenarios and parameter settings when using a single cytostatic drug. In general, adaptive therapy works best under conditions of higher fitness cost, higher replacement rate, higher turnover. Adaptive therapy works best when drug dosages are changed as soon as a change in tumor burden is detected. In general, it is better to pause treatment sooner than later, when the tumor is shrinking If the amount of drug used is too low, it is unable to control the sensitive cells and the tumor grows. However, if the drug dose is too high, it quickly selects for resistant cells and eventually the tumor grows out of control. However, there appears to be intermediate levels of dosing, which we call the minimum effective dose, which is able to control the sensitive cells but is not high enough to select for the resistant cells to grow out of control.

bioinformatics↗

bioRxiv2023

In Silico Investigations of Adaptive Therapy Using Two Cytotoxic or Two Cytostatic Drugs

While the dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression) involves adjusting drug dosages when the tumor burden changes, the fixed-dose (FD) protocols involves administering a specific, constant dosage of the drug only when the tumor is growing (Dose-Skipping) or when the absolute tumor burden is above the baseline level until it reduces to a certain percentage of the baseline (Intermittent). Moreover, two different drugs can be administered simultaneously (cocktail), or the drugs can be switched such that only one drug is applied at a given time (ping-pong), either every cycle (ping-pong alternate every cycle) or when the tumor grows (ping-pong on progression). The dose modulation protocols work well when treated with two cytotoxic drugs, however, the ping-pong protocols (DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression, FD Ping-Pong Intermittent) work well when treated with two cytostatic drugs. In general, adaptive therapy, using either two cytotoxic or two cytostatic drugs works best under conditions of high competition, such as high fitness cost, high replacement rates, and high turnover, although treatment using two cytostatic drugs works best under low turnover in many cases. Adaptive therapy works best when drug dosages are changed as soon as a change in tumor burden is detected, and it is best to pause treatment sooner rather than later when the tumor is shrinking. Adaptive therapy works best when an intermediate level of drug dosage is used, and both treatment with too little or too much drug leads to poor survival outcome.

bioinformatics↗

bioRxiv2021

The Human Tumor Atlas Network (HTAN) Breast PreCancer Atlas: A multi-omic integrative analysis of ductal carcinoma in situ with clinical outcomes

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We identified 812 genes associated with ipsilateral recurrence within 5 years from treatment and developed a classifier that was predictive of DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions were identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. HIGHLIGHTS Development of a new classifier for DCIS recurrence or progression Outcome associated pathways identified across multiple data types and compartments Four stroma-specific signatures identified CNAs characterize DCIS subgroups associated with high risk invasive cancers

cancer biology↗

bioRxiv2018

Cryptsim: Modeling the evolutionary dynamics of the progression of Barrett’s esophagus to esophageal adenocarcinoma

To alleviate the over-diagnosis and overtreatment of premalignant conditions we need to predict their progression to cancer, and therefore, the dynamics of an evolutionary process. However, monitoring evolutionary processes in vivo is extremely challenging. Computer simulations constitute an attractive alternative, allowing us to study these dynamics based on a set of evolutionary parameters.\n\nWe introduce CryptSim, a simulator of crypt evolution inspired by Barretts esophagus. We detail the most relevant computational strategies it implements, and perform a simulation study showing that the interaction between neighboring crypts may play a crucial role in carcinogenesis.

cancer biology↗

bioRxiv2017

Phylostratigraphic analysis of tumor and developmental transcriptomes reveals relationship between oncogenesis, phylogenesis and ontogenesis

The question of the existence of cancer is inadequately answered by invoking somatic mutations or the disruptions of cellular and tissue control mechanisms. As such uniformly random events alone cannot account for the almost inevitable occurrence of an extremely complex process such as cancer. In the different epistemic realm, an ultimate explanation of cancer is that cancer is a reversion of a cell to an ancestral pre-Metazoan state, i.e. a cellular form of atavism. Several studies have suggested that genes involved in cancer have evolved at particular evolutionary time linked to the unicellular-multicellular transition. Here we used a refined phylostratigraphic analysis of evolutionary ages of the known genes/pathways associated with cancer and the genes differentially expressed between normal and cancer tissue as well as between embryonic and mature (differentiated) cells. We found that cancer-specific transcriptomes and cancer-related pathways were enriched for genes that evolved in the pre-Metazoan era and depleted of genes that evolved in the post-Metazoan era. By contrast an opposite relation was found for cell maturation: the age distribution frequency of the genes expressed in differentiated epithelial cells were enriched for post-Metazoan genes and depleted of pre-Metazoan ones. These findings support the atavism theory that cancer cells manifest the reactivation of an ancient ancestral state featuring unicellular modalities. Thus our bioinformatics analyses suggest that not only does oncogenesis recapitulate ontogenesis, and ontogenesis recapitulates phylogenesis, but also oncogenesis recapitulates phylogenesis. This more encompassing perspective may offer a natural organizing framework for genetic alterations in cancers and point to new treatment options that target the genes controlling the atavism transition.\n\nOne Sentence SummaryTracing cancer gene evolutionary ages revealed that cancer reverts to a pre-existing early Metazoan state.

cancer biology↗