Biology subjects

Sinha, S.

Publications and source records attributed to Sinha, S..

At least 199 records · Page 11Linked to original sources

bioRxiv2020

New insights into nCOVID-19 binding domain and its cellular receptors

nCOVID-19 virus makes cellular entry using its spike protein protruding out on its surface. Angiotensin converting enzyme 2 receptor has been identified as a receptor that mediates the viral entry by binding with the receptor binding motif of spike protein. In the present study, we elucidate the significance of N-terminal domain of spike protein in spike-receptor interactions. Recent clinical reports indicate a link between nCOVID-19 infections with patient comorbidities. The underlying reason behind this relationship is not clear. Using molecular docking, we study the affinity of the nCOVID-19 spike protein with cell receptors overexpressed under disease conditions. Our results suggest that certain cell receptors such as DC/L-SIGN, DPP4, IL22R and ephrin receptors could act as potential receptors for the spike protein. The receptor binding domain of nCOVID-19 is more flexible than that of SARS-COV and has a high propensity to undergo phase separation. Higher flexibility of nCOVID-19 receptor binding domain might enable it to bind multiple receptor partners. Further experimental work on the association of these receptors with spike protein may help us to explain the severity of nCOVID-19 infection in patients with comorbidities.

bioinformatics↗

bioRxiv2020

Self-generated persistent random forces drive phase separation in growing tumors

A single solid tumor, composed of nearly identical cells, exhibits heterogeneous dynamics. Cells dynamics in the core is glass-like whereas those in the periphery undergo diffusive or super-diffusive behavior. Quantification of heterogeneity using the mean square displacement or the self-intermediate scattering function, which involves averaging over the cell population, hides the complexity of the collective movement. Using the t-distributed stochastic neighbor embedding (t-SNE), a popular unsupervised machine learning dimensionality reduction technique, we show that the phase space structure of an evolving colony of cells, driven by cell division and apoptosis, partitions into nearly disjoint sets composed principally of core and periphery cells. The non-equilibrium phase separation is driven by the differences in the persistence of self-generated active forces induced by cell division. Extensive heterogeneity revealed by t-SNE paves way towards understanding the origins of intratumor heterogeneity using experimental imaging data.

biophysics↗

bioRxiv2020

Brain-wide electrical dynamics encode an appetitive socioemotional state

Many cortical and subcortical regions contribute to complex social behavior; nevertheless, the network level architecture whereby the brain integrates this information to encode appetitive socioemotional behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover an explainable brain network that encodes the extent to which mice chose to engage another mouse. This socioemotional network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on ventral tegmental area, and network activity is synchronized with brain-wide cellular firing. The network generalizes, on a mouse-by-mouse basis, to encode socioemotional behaviors in healthy animals, but fails to encode an appetitive socioemotional state in a high confidence genetic mouse model of autism. Thus, our findings reveal the architecture whereby the brain integrates spatially distributed activity across timescales to encode an appetitive socioemotional brain state in health and disease.

neuroscience↗

bioRxiv2020

A unified mechanism for spatiotemporal patterns in somitogenesis

Somitogenesis, the process of body segmentation during embryonic development, exhibits a key set of features that is conserved across all vertebrate species despite differences in the detailed mechanisms. Prior to the formation of somites along the pre-somitic mesoderm (PSM), periodic expression of clock genes is observed in its constituent cells. As the PSM expands through the addition of new cells at its posterior, the oscillations in the cells closer to the anterior cease and eventually lead to formation of rostral and caudal halves of the somites. This pattern formation is believed to be coordinated by interactions between neighboring cells via receptor-ligand coupling. However, the mechanism underlying the transition from synchronized oscillations to traveling waves and subsequent arrest of activity, followed by the appearance of polarized somites, has not yet been established. In this paper we have proposed a unified mechanism that reproduces the sequence of dynamical transitions observed during somitogenesis by combining the local interactions mediated via Notch-Delta intercellular coupling with global spatial heterogeneity introduced through a morphogen gradient that is known to occur along the anteroposterior axis of the growing PSM. Our model provides a framework that integrates a boundary-organized pattern formation mechanism, which uses positional information provided by a morphogen gradient, with the coupling-mediated self-organized emergence of collective dynamics, to explain the processes that lead to segmentation.

developmental biology↗

bioRxiv2020

Far from equilibrium dynamics of tracer particles embedded in a growing multicellular spheroid

Local stresses on the cancer cells (CCs) have been measured by embedding inert tracer particles (TPs) in a growing multicellular spheroid. The utility of the experiments requires that the TPs do not alter the CC microenvironment. We show, using theory and extensive simulations, that proliferation and apoptosis of the CCs, drive the dynamics of the TPs far from equilibrium. On times less than the CC division times, the TPs exhibit sub-diffusive behavior (the mean square displacement, [Formula] with {beta}TP < 1). Surprisingly, in the long-time limit, the motion of the TPs is hyper-diffusive ([Formula] with TP > 2) due to persistent directed motion for a number of CC division times. In contrast, CC proliferation randomizes their motion resulting from jamming at short times to super-diffusive behavior, with CC exceeding unity, at long times. Surprisingly, the effect of the TPs on CC dynamics and radial pressure is negligible, suggesting that the TPs are reliable reporters of the CC microenvironment.

biophysics↗

bioRxiv2020

An Envelope Protein of a Prokaryotic Organelle Function towards Conserving Catalytic Activity of the Native Enzyme at Higher Temperatures

A classic example of an all-protein natural nano-bioreactor, the bacterial microcompartments are a special kind of prokaryotic organelles that confine enzymes within a small volume enveloped by an outer layer of shell proteins. This arrangement provides conditional metabolic aid to the bacteria. The outer shell allows selective diffusion of small molecules and sequesters toxic metabolites. In this work we use 1,2-propanediol utilization microcompartment as a model to study the effect of molecular confinement on the stability and catalytic activity of native enzymes in microcompartment. We observe a 50% decrease in the activity of free enzyme PduCDE at 45{degrees}C, while PduMCP retains its optimum activity till 50{degrees}C followed by more than 40% reduced activity at 55{degrees}C. PduBB, the major component of the outer shell contributes to the increased catalytic activity of PduCDE. PduBB also prevents the unfolding and aggregation of PduCDE under thermal stress. Using a combination of experimental and theoretical studies we probe the interactions of the shell proteins PduBB, N-terminal truncated PduB and single mutant PduBM38L with PduCDE. We observe that all the three variants of PduB* shell proteins interact with the enzyme in vitro, but only PduBB influences its activity and stability, underscoring the significance of the unique combination of PduB and PduB in PduMCP assembly.

molecular biology↗

bioRxiv2020

Whole exome precision oncology targeting synthetic lethal vulnerabilities across the tumor transcriptome

Precision oncology has made significant advances in the last few years, mainly by targeting actionable mutations in cancer driver genes. However, the proportion of patients whose tumors can be targeted therapeutically remains limited. Recent studies have begun to explore the benefit of analyzing tumor transcriptomics data to guide patient treatment, raising the need for new approaches for systematically accomplishing that. Here we show that computationally derived genetic interactions can successfully predict patient response. Assembling a broad repertoire of 32 datasets spanning more than 1,500 patients and including both tumor transcriptomics and response data, we predicted the response in 17 out of 21 targeted and 8 out of 11 checkpoint therapy datasets across 8 different cancer types with considerable accuracy, without ever training on these datasets. Analyzing the recently published multi-arm WINTHER trial, we show that the fraction of patients benefitting from transcriptomic-based treatments could potentially be markedly increased from 15% to about 85% by targeting synthetic lethal vulnerabilities in their tumors. In summary, this is the first computational approach to obtain considerable predictive performance across many different targeted and immunotherapy datasets, providing a promising new way for guiding cancer treatment based on the tumor transcriptomics of cancer patients.

cancer biology↗

bioRxiv2020

Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms

It is challenging to predict the impact of small genetic changes such as single nucleotide polymorphisms on gene expression, since mechanisms involved in gene regulation and their cis-regulatory encoding are not well-understood. Recent studies have attempted to predict the functional impact of non-coding variants based on available knowledge of cis-regulatory encoding, e.g., transcription factor (TF) motifs. In this work, we explore the relationship between regulatory variants and cis-regulatory encoding from the opposite angle, using the former to inform the latter. We employ sequence-to-expression modeling to resolve ambiguities regarding gene regulatory mechanisms using information about effects of single nucleotide variations in an enhancer. We demonstrate our methodology using a well-studied enhancer of the developmental gene intermediate neuroblasts defective (ind) in D. melanogaster. We first trained the thermodynamics-based model GEMSTAT to relate the neuroectodermal expression pattern of ind to its enhancers sequence, and constructed an ensemble of models that represent different parameter settings consistent with available data for this gene. We then predicted the effects of every possible single nucleotide variation within this enhancer, and compared these to SNP data recorded in the Drosophila Genome Reference Panel. We chose specific SNPs for which different models in the ensemble made conflicting predictions, and tested their effect in vivo. These experiments narrowed in on one mechanistic model as capable of explaining the observed effects. We further confirmed the generalizability of this model to orthologous enhancers and other related developmental enhancers. In conclusion, mechanistic models of cis-regulatory function not only help make specific predictions of variant impact, they may also be learned more accurately using data on variants. STATEMENT OF SIGNIFICANCEA central issue in analyzing variations in the non-coding genome is to interpret their functional impact, and their connections to phenotype differences and disease etiology. Machine learning methods based on statistical modeling have been developed to associate genetic variants to expression changes. However, associations predicted by these models may not be functionally relevant, despite being statisticaly significant. We describe how mathematical modeling of gene expression can be employed to systematically study the non-coding sequence and its relationship to gene expression. We demonstrate our method in a well studied developmental enhancer of the fruitfly. We establish the efficacy of mathematical models in combination with the polymorphism data to reveal new mechanistic insights.

bioinformatics↗

bioRxiv2020

GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis

MotivationA common but critical task in genomic data analysis is finding features that separate and thereby help explain differences between two classes of biological objects, e.g., genes that explain the differences between healthy and diseased patients. As lower-cost, high-throughput experimental methods greatly increase the number of samples that are assayed as objects for analysis, computational methods are needed to quickly provide insights into high-dimensional datasets with tens of thousands of objects and features. ResultsWe develop an interactive exploration tool called GO_SCPLOWENVISAGEC_SCPLOW that rapidly discovers the most discriminative feature pairs that best separate two classes in a dataset, and displays the corresponding visualizations. Since quickly finding top feature pairs is computationally challenging, especially when the numbers of objects and features are large, we propose a suite of optimizations to make GO_SCPLOWENVISAGEC_SCPLOW more responsive and demonstrate that our optimizations lead to a 400X speedup over competitive baselines for multiple biological data sets. With this speedup, GO_SCPLOWENVISAGEC_SCPLOW enables the exploration of more large-scale datasets and alternate hypotheses in an interactive and interpretable fashion. We apply GO_SCPLOWENVISAGEC_SCPLOW to uncover pairs of genes whose transcriptomic responses significantly discriminate treatments of several chemotherapy drugs. AvailabilityFree webserver at http://genvisage.knoweng.org:443/ with source code at https://github.com/KnowEnG/Genvisage

bioinformatics↗

bioRxiv2020

Division of labor between YAP and TAZ in non-small cell lung cancer

Lung cancer is the leading cause of cancer-related deaths worldwide. The paralogous transcriptional cofactors Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ, also called WWTR1), the main downstream effectors of the Hippo signal transduction pathway, are emerging as pivotal determinants of malignancy in lung cancer. Traditionally, studies have tended to consider YAP and TAZ as functionally redundant transcriptional cofactors, with similar biological impact. However, there is growing evidence that each of them also possesses distinct attributes. Here, we sought to systematically characterize the division of labor between YAP and TAZ in non-small cell lung cancer (NSCLC), the most common histological subtype of lung cancer. Employing representative NSCLC cell lines, as well as patient-derived data, we show that the two paralogs orchestrate non-overlapping transcription programs in this cancer type: whereas YAP preferentially regulates gene sets associated with cell division and cell cycle progression, TAZ preferentially regulates genes associated with extracellular matrix organization. Concordantly, depletion of YAP, but not TAZ, leads to growth arrest, while YAP overexpression promotes cell proliferation. Likewise, depletion of TAZ, but not YAP, compromises cell migration, whereas TAZ overexpression enhances migration. Importantly, the differential effects of YAP vs TAZ on key cellular processes are also associated with differential response to anti-cancer therapies. Uncovering the different activities and downstream effects of YAP and TAZ may thus facilitate better stratification of lung cancer patients for anti-cancer therapies.

cancer biology↗

bioRxiv2019

Spatially heterogeneous dynamics of cells in a growing tumor spheroid: Comparison between Theory and Experiments

Collective cell movement, characterized by multiple cells that are in contact for substantial periods of time and undergo correlated motion, plays a central role in cancer and embryogenesis. Recent imaging experiments have provided time-dependent traces of individual cells, thus providing an unprecedented picture of tumor spheroid growth. By using simulations of a minimal cell model, we analyze the experimental data that map the movement of cells in fibrosarcoma tumor spheroid embedded in a collagen matrix. Both simulations and experiments show that cells in the core of the spheroid exhibit subdiffusive glassy dynamics (mean square displacement, {Delta}(t) {approx} t with < 1), whereas cells in the periphery exhibit superdiffusive motion, {Delta}(t) {approx} t with > 1). The motion of most of the cells near the periphery undergo highly persistent and correlated directional motion, thus explaining the observed superdiffusive behavior. The values for cells in the core and periphery, extracted from simulations and experiments are in near quantitative agreement with each other, which is surprising given that no parameter in the model was used to fit the measurements. The qualitatively different dynamics of cells in the core and periphery is captured by the fourth order susceptibility, introduced to characterize metastable states in glass forming systems. Analyses of the velocity autocorrelation of individual cells show remarkable spatial heterogeneity with no two cells exhibiting similar behavior. The prediction that should depend on the location of the cells in the tumor is amenable to experimental test. The highly heterogeneous dynamics of cells in the tumor spheroid provides a plausible mechanism for the origin of intratumor heterogeneity.

biophysics↗

bioRxiv2019

Addressing age related hearing loss through engineering accessible and affordable hearing technology

Hearing Aids have dominated the audiological market for decades. While the costs of the electronic components have reduced substantially, the cost of a hearing aid has risen steadily to the point that it has become unaffordable for the vast majority of the population with Age Related Hearing Loss (ARHL). Here, we present an ultra-low-cost, affordable and accessible hearing aid device ( LoCHAid), specifically targeted for ARHL in elderly patients. The LoCHAid costs only 98 cents (< $1) to mass manufacture and can be personalized for each user through a 3D-printable case. It is designed to be an over-the-counter (OTC) self-serviceable solution for elderly individuals with ARHL. Electroacoustic measurements show that the device meets most of the targets set out by the WHO Preferred Product Profile and Consumer Technology Association for Hearing Aids. The frequency response of the hearing aid shows selectable gain in the range of 4-8 kHz, and mild to moderate gain between 200-1000 Hz, and shows very limited total distortion (1%). Simulated gain measurements show that the LoCHAid is well fitted to a range of ARHL profiles for males and females between the ages of 60-79 years. Overall, the measurements show that the device has the potential to benefit individuals with ARHL. Thus, our proposed design addresses a long-standing and grand challenge of affordable and accessible hearing technology for every elderly person on this planet.

bioengineering↗

bioRxiv2019

Gene variants in ZGRF1 implicated for a rare sensory reflex epilepsy

Hot water epilepsy (HWE) is a sensory reflex epilepsy in which seizures are precipitated by a stimulus such as contact with hot water. While there is a genetic basis to its etiology, identity of the genes underlying this relatively uncommon disorder has remained unknown. Here, we present the results of our studies aimed at identifying a causative gene in a south Indian four-generation family with several affected members. We conducted whole-exome sequencing and examined a known locus that maps to 4q24-q28 (HWE2, MIM: 613340) that we had previously identified. We identified a sequence variant, c.1805C>T (p.Thr602Ile) in ZGRF1, located within the HWE2 locus, co-segregating with the disorder. The transcript structure of ZGRF1 was examined in 288 HWE patients, and five additional missense variants, Arg326Gln, Glu660Gly, Arg1862*, Phe1940Leu and Asp1984Gly, present exclusively or almost exclusively in the patients were found. Functional correlates of the six variants identified were examined in cultured mammalian cells. We observed spindle pole defects during cell division and partially disrupted localization of UPF1, a protein involved in cell cycle regulation, at the spindles. Our observations provide insights into the genetic basis of HWE and suggest the involvement of hitherto unanticipated molecular process in this disorder.

genetics↗

bioRxiv2019

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue, but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients.\n\nWe designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide significantly accurate prediction. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.\n\nAUTHOR SUMMARYCancer is among the leading causes of death globally and perdition of the drug response of patients to different treatments based on their clinical and molecular profiles can enable individualized cancer medicine. Machine learning algorithms have the potential to play a significant role in this task; but, these algorithms are designed based the premise that a large number of labeled training samples are available, and these samples are accurate representation of the profiles of real tumors. However, due to ethical and technical reasons, it is not possible to screen humans for many drugs, significantly limiting the size of training data. To overcome this data scarcity problem, machine learning models can be trained using large databases of preclinical samples (e.g. cancer cell line cultures). However, due to the major differences between preclinical samples and real tumors, it is unclear how accurately such preclinical-to-clinical computational models can predict the clinical drug response of cancer patients.\n\nHere, first we systematically evaluate a variety of different linear and nonlinear machine learning algorithms for this particular task using two large databases of preclinical (GDSC) and tumor samples (TCGA). Then, we present a novel method called TG-LASSO that utilizes a new approach for explicitly incorporating the tissue of origin of samples in the prediction task. Our results show that TG-LASSO outperforms all other algorithms and can accurately distinguish resistant and sensitive patients for the majority of the tested drugs. Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity in each cancer type.

bioinformatics↗

bioRxiv2019

A single-cell expression simulator guided by gene regulatory networks

A common approach to benchmarking of single-cell transcriptomics tools is to generate synthetic data sets that resemble experimental data in their statistical properties. However, existing single-cell simulators do not incorporate known principles of transcription factor-gene regulatory interactions that underlie expression dynamics. Here we present SERGIO, a simulator of single-cell gene expression data that models the stochastic nature of transcription as well as linear and non-linear influences of multiple transcription factors on genes according to a user-provided gene regulatory network. SERGIO is capable of simulating any number of cell types in steady-state or cells differentiating to multiple fates according to a provided trajectory, reporting both unspliced and spliced transcript counts in single-cells. We show that data sets generated by SERGIO are comparable with experimental data in terms of multiple statistical measures. We also illustrate the use of SERGIO to benchmark several popular single-cell analysis tools, including GRN inference methods.

bioinformatics↗

bioRxiv2019

Dual Role of Cell-Cell Adhesion In Tumor Suppression and Proliferation

It is known that mechanical interactions couple a cell to its neighbors, enabling a feedback loop to regulate tissue growth. However, the interplay between cell-cell adhesion strength, local cell density and force fluctuations in regulating cell proliferation is poorly understood. Here, we show that spatial variations in the tumor growth rates, which depend on the location of cells within tissue spheroids, are strongly influenced by cell-cell adhesion. As the strength of the cell-cell adhesion increases, intercellular pressure initially decreases, enabling dormant cells to more readily enter into a proliferative state. We identify an optimal cell-cell adhesion regime where pressure on a cell is a minimum, allowing for maximum proliferation. We use a theoretical model to validate this novel collective feedback mechanism coupling adhesion strength, local stress fluctuations and proliferation. Our results predict the existence of a non-monotonic proliferation behavior as a function of adhesion strength, consistent with experimental results. Several experimental implications of the proposed role of cell-cell adhesion in proliferation are quantified, making our model predictions amenable to further experimental scrutiny. We show that the mechanism of contact inhibition of proliferation, based on a pressure-adhesion feedback loop, serves as a unifying mechanism to understand the role of cell-cell adhesion in proliferation.

cancer biology↗

bioRxiv2019

Variable Mutations at the p53-R273 Oncogeic Hotspot Position Leads to Altered Properties

Mutations in p53 protein, especially in the DNA binding domain is one of the major hallmarks of cancer. The R273 position is a DNA contact position and has several oncogenic variants. Surprisingly, cancer patients carrying different mutant-variants of R273 in p53 have different survival rates indicating that the DNA contact inhibition may not be the sole reason for reduced survival with R273 variants. Here, we probed the structural properties of three major oncogenic variants of the R273: ([R273L], [R273H], and [R273C])p53. Using a series of biophysical, biochemical and theoretical simulation studies, we observe that these oncogenic variants of the p53 not only suffer a loss in DNA binding, but also show distinct structural stabilty, aggregation and toxicity profiles. [R273C]p53 shows maximum amyloidogenicity while [R273L]p53 shows maximum aggregation. Further probe in the aggregation mechanism show that [R273C]p53 aggregation is disulphide mediated whereas hydrophobic interactions dominate self-assembly in [R273L]p53. MD simulation studies clearly show that -helical intermediates are observed in [R273C]p53 whereas {beta}-sheets are observed for [R273L]p53. Our study indicates that each of the R273 variant has its own distinct property of stability and self-assembly, the molecular basis of which, may lead to different types of cancer pathogenesis in vivo. These studies will aid the design of therapeutic strategies for cancer using residue specific or process specific protein aggregation as target.\n\nStatement of significanceThe present work stems from an interesting observation that genetic mutations that results in switching of one amino acid to different variants at the same codon show different cancer cell progression. We are trying to understand the molecular reason behind the different gain-of-function opted by these variants. With the help of biophysical and biochemical experiments, and computational studies we have observed that the different thermal stability, unique mechanism of unfolding and self-assembly might be one of the crucial parameters for their different oncogenic effect. These studies thus call for the need of developing therapeutic strategies that consider the resultant mutant-variant as a target rather than mutation position. This is an important lead towards the understanding of cancer.

biophysics↗

bioRxiv2019

Higher prevalence of homologous recombination-deficiency in lung squamous carcinoma from African Americans

To improve our understanding of the longstanding disparities in incidence and mortality across multiple cancer types among minority populations, we performed a systematic comparative analysis of molecular features in tumors from African American (AA) and European American (EA) ancestry. Our pan-cancer analysis on the cancer genome atlas (TCGA) and a more focused analysis of genome-wide somatic copy number profiles integrated with tumor-normal RNA sequencing in a racially balanced cohort of 222 non-small cell lung cancers (NSCLC) reveals more aggressive genomic characteristics of AA tumors. In general, we find AA tumors exhibit higher genomic instability (GI), homologous recombination-deficiency (HRD) levels, and more aggressive molecular features such as chromothripsis across many cancer types, including lung squamous carcinoma (LUSC). GI and HRD levels are strongly correlated across AA tumors, indicating that HRD plays an important role in GI in these patients. The prevalence of germline HRD is higher in AA tumors, suggesting that the somatic differences observed have genetic ancestry origins. Finally, we identify AA-specific copy number-based arm, focal and gene level recurrent features in lung cancer, including a higher frequency of PTEN deletion and KRAS amplification and a lower frequency of CDKN2A deletion. These results highlight the importance of including minority and under-represented populations in genomics research and may have therapeutic implications.

cancer biology↗