Matrix and tensor comparisons of genomic profiles to predict cancer survival and drug targets

disseratationDespite recent large-scale profiling efforts, the best predictor of a glioblastoma (GBM) brain cancer patient's survival remains the patient's age at diagnosis. The best predictor of an ovarian serous cystadenocarcinoma (OV) patient's survival remains the tumor's stage, an assessment - numbering I to IV - of the spread of the cancer. To identify DNA copy-number alterations (CNAs) that might predict GBM or OV patients' survival, we comparatively modeled matched genomic profiles from The Cancer Genome Atlas (TCGA). Generalized singular value decomposition (GSVD) of patient-matched but probe- independent GBM and normal profiles uncovered a previously unknown global pattern of tumor-exclusive co-occurring CNAs that is correlated, and possibly causally related to, GBM patients' survival and response to chemotherapy. This suggests that the GBM survival phenotype is an outcome of its global genotype. The GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern variations that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations, without a-priori knowledge of these variations. The pattern is independent of age, and combined with age, makes a better predictor than age alone. The pattern suggests previously unrecognized targets for personalized GBM drug therapy, the kinase TLK2 and the methyltransferase METTL2A. A novel tensor GSVD of patient- and platform-matched OV and normal genomic profiles revealed multiple chromosome arm-wide patterns of CNAs that are correlated with OV patients' survival. These indicate several, previously unrecognized, subtypes of OV. The tensor GSVD is an exact simultaneous decomposition of two high-dimensional datasets arranged in higher-order tensors. The tensor GSVD generalizes the GSVD, which is limited to two second-order tensors, i.e., matrices. The chromosome arm-wide patterns of CNAs are independent of the OV tumor stage. Combined with stage, each of the patterns makes a better predictor than stage alone. We conclude that the GSVD and the novel tensor GSVD can uncover the relations, and possibly causal coordinations, between different recorded aspects of the same medical phenomenon. GSVD and tensor GSVD comparisons can be used to determine one patient's medical status in relation to other patients in a set, and inform the patient's prognosis, and possibly also treatment

Master of Science

thesisDespite recent large-scale profiling efforts, the best prognostic predictor of glioblastoma multiforme (GBM) remains the patient's age at diagnosis. We describe a global pattern of tumor-exclusive co-occurring copy-number alterations (CNAs) that is correlated, possibly coordinated with GBM patients' survival and response to chemotherapy. The pattern is revealed by generalized singular value decomposition (GSVD) comparison of patient-matched but probeindependent GBM and normal aCGH datasets from The Cancer Genome Atlas (TCGA). We find that, first, the GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern copynumber variations (CNVs) that occur in the normal human genome (e.g., femalespecific X chromosome amplification) and experimental variations (e.g., in tissue batch, genomic center, hybridization date and scanner), without a-priori knowledge of these variations. Second, the pattern includes most known GBMassociated changes in chromosome numbers and focal CNAs, as well as several previously unreported CNAs in > 3% of the patients. These included the biochemically putative drug target, cell cycle-regulated serine/threonine kinaseencoding TLK2, the cyclin El-encoding CCNE1, and the Rb-binding histone demethylase-encoding KDM5A. Third, the pattern provides a better prognostic predictor than the chromosome numbers or any one focal CNA that it identifies, suggesting that the GBM survival phenotype is an outcome of its global genotype. The pattern is independent of age, and combined with age, makes a better predictor than age alone. GSVD comparison of matched profiles of a larger set of TCGA patients, inclusive of the initial set, confirms the global pattern. GSVD classification of the GBM profiles of an independent set of patients validates the prognostic contribution of the pattern

Affordable voltammetric sensor based on anodized disposable pencil graphite electrodes for sensitive determination of dopamine and uric acid in presence of high concentration of ascorbic acid

A simple, disposable and low - cost voltammetric sensor based on the anodized pencil graphite electrode (APGE) for the simultaneous determination of dopamine (DA) and uric acid (UA) is demonstrated. The physico-chemical properties of the pencil graphite electrode (PGE) before and after anodization were analyzed using FT-IR, FT-Raman, SEM and EIS characterization techniques. In comparison to PGE, APGE exhibited excellent electrochemical activity towards the simultaneous detection of DA and UA with peak-to-peak separation of about 0.18 V even in the presence of high concentration (2 mM) of ascorbic acid (AA). The discrimination of APGE towards AA was rationalized through the absence of favorable surface interactions between oxygen rich functional groups on the surface of APGE and AA. Using DPV without any pre-concentration step and under optimized conditions, APGE displayed a linear range of 1 – 80 μM with an estimated limit of detection (LOD, 3σ/m) of 0.008 μM and 0.014 μM for DA and UA, respectively. Moreover, a higher sensitivity in comparison to other previously reported pretreated pencil graphite electrodes was observed for DA (34.32 μA/μM) and UA (12.33 μA/μM). The practical applicability of APGE was demonstrated through the estimation of DA in human blood serum and UA in urine samples

GSVD Comparison of Patient-Matched Normal and Tumor aCGH Profiles Reveals Global Copy-Number Alterations Predicting Glioblastoma Multiforme Survival

Despite recent large-scale profiling efforts, the best prognostic predictor of glioblastoma multiforme (GBM) remains the patient's age at diagnosis. We describe a global pattern of tumor-exclusive co-occurring copy-number alterations (CNAs) that is correlated, possibly coordinated with GBM patients' survival and response to chemotherapy. The pattern is revealed by GSVD comparison of patient-matched but probe-independent GBM and normal aCGH datasets from The Cancer Genome Atlas (TCGA). We find that, first, the GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern copy-number variations (CNVs) that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations (e.g., in tissue batch, genomic center, hybridization date and scanner), without a-priori knowledge of these variations. Second, the pattern includes most known GBM-associated changes in chromosome numbers and focal CNAs, as well as several previously unreported CNAs in 3% of the patients. These include the biochemically putative drug target, cell cycle-regulated serine/threonine kinase-encoding TLK2, the cyclin E1-encoding CCNE1, and the Rb-binding histone demethylase-encoding KDM5A. Third, the pattern provides a better prognostic predictor than the chromosome numbers or any one focal CNA that it identifies, suggesting that the GBM survival phenotype is an outcome of its global genotype. The pattern is independent of age, and combined with age, makes a better predictor than age alone. GSVD comparison of matched profiles of a larger set of TCGA patients, inclusive of the initial set, confirms the global pattern. GSVD classification of the GBM profiles of an independent set of patients validates the prognostic contribution of the pattern

Multi-center feasibility study evaluating recruitment, variability in risk factors and biomarkers for a diet and cancer cohort in India

<p>Abstract</p> <p>Background</p> <p>India's population exhibits diverse dietary habits and chronic disease patterns. Nutritional epidemiologic studies in India are primarily of cross-sectional or case-control design and subject to biases, including differential recall of past diet. The aim of this feasibility study was to evaluate whether a diet-focused cohort study of cancer could be established in India, providing insight into potentially unique diet and lifestyle exposures.</p> <p>Methods</p> <p>Field staff contacted 7,064 households within three regions of India (New Delhi, Mumbai, and Trivandrum) and found 4,671 eligible adults aged 35-69 years. Participants completed interviewer-administered questionnaires (demographic, diet history, physical activity, medical/reproductive history, tobacco/alcohol use, and occupational history), and staff collected biological samples (blood, urine, and toenail clippings), anthropometric measurements (weight, standing and sitting height; waist, hip, and thigh circumference; triceps, sub-scapula and supra-patella skin fold), and blood pressure measurements.</p> <p>Results</p> <p>Eighty-eight percent of eligible subjects completed all questionnaires and 67% provided biological samples. Unique protein sources by region were fish in Trivandrum, dairy in New Delhi, and pulses (legumes) in Mumbai. Consumption of meat, alcohol, fast food, and soft drinks was scarce in all three regions. A large percentage of the participants were centrally obese and had elevated blood glucose levels. New Delhi participants were also the least physically active and had elevated lipids levels, suggesting a high prevalence of metabolic syndrome.</p> <p>Conclusions</p> <p>A high percentage of participants complied with study procedures including biological sample collection. Epidemiologic expertise and sufficient infrastructure exists at these three sites in India to successfully carry out a modest sized population-based study; however, we identified some potential problems in conducting a cohort study, such as limited number of facilities to handle biological samples.</p

Tensor GSVD of Patient- and Platform-Matched Tumor and Normal DNA Copy-Number Profiles Uncovers Chromosome Arm-Wide Patterns of Tumor-Exclusive Platform-Consistent Alterations Encoding for Cell Transformation and Predicting Ovarian Cancer Survival

<div><p>The number of large-scale high-dimensional datasets recording different aspects of a single disease is growing, accompanied by a need for frameworks that can create one coherent model from multiple tensors of matched columns, e.g., patients and platforms, but independent rows, e.g., probes. We define and prove the mathematical properties of a novel tensor generalized singular value decomposition (GSVD), which can simultaneously find the similarities and dissimilarities, i.e., patterns of varying relative significance, between any two such tensors. We demonstrate the tensor GSVD in comparative modeling of patient- and platform-matched but probe-independent ovarian serous cystadenocarcinoma (OV) tumor, mostly high-grade, and normal DNA copy-number profiles, across each chromosome arm, and combination of two arms, separately. The modeling uncovers previously unrecognized patterns of tumor-exclusive platform-consistent co-occurring copy-number alterations (CNAs). We find, first, and validate that each of the patterns across only 7p and Xq, and the combination of 6p+12p, is correlated with a patient’s prognosis, is independent of the tumor’s stage, the best predictor of OV survival to date, and together with stage makes a better predictor than stage alone. Second, these patterns include most known OV-associated CNAs that map to these chromosome arms, as well as several previously unreported, yet frequent focal CNAs. Third, differential mRNA, microRNA, and protein expression consistently map to the DNA CNAs. A coherent picture emerges for each pattern, suggesting roles for the CNAs in OV pathogenesis and personalized therapy. In 6p+12p, deletion of the p21-encoding <i>CDKN1A</i> and p38-encoding <i>MAPK14</i> and amplification of <i>RAD51AP1</i> and <i>KRAS</i> encode for human cell transformation, and are correlated with a cell’s immortality, and a patient’s shorter survival time. In 7p, <i>RPA3</i> deletion and <i>POLD2</i> amplification are correlated with DNA stability, and a longer survival. In Xq, <i>PABPC5</i> deletion and <i>BCAP31</i> amplification are correlated with a cellular immune response, and a longer survival.</p></div

Survival analyses of the discovery and validation sets of patients classified by tensor GSVD, or tensor GSVD and tumor stage at diagnosis.

<p>(<i>a</i>) Kaplan-Meier (KM) curves of the discovery set of 249 patients classified by the 6p+12p <i>x</i>-probelet coefficient, show a median survival time difference of 11 months, with the corresponding log-rank test <i>P</i>-value < 10⁻². The univariate Cox proportional hazard ratio is 1.7. (<i>b</i>) Survival analyses of the 249 patients classified by the 7p <i>x</i>-probelet coefficient. (<i>c</i>) The 249 patients classified by the Xq <i>x</i>-probelet coefficient. (<i>d</i>) The 249 patients classified by both the 6p+12p tensor GSVD and tumor stage at diagnosis, show the bivariate Cox hazard ratios of 1.5 and 4.0, which do not differ significantly from the corresponding univariate hazard ratios of 1.7 and 4.4, respectively. This means that the 6p+12p tensor GSVD is independent of stage, the best predictor of OV survival to date. The 61 months KM median survival time difference is about 85% and more than two years greater than the 33 month difference between the patients classified by stage alone. This means that the tensor GSVD and stage combined make a better predictor than stage alone. (<i>e</i>) The 249 patients classified by both the 7p tensor GSVD and stage. (<i>f</i>) The 249 patients classified by both the Xq tensor GSVD and stage. (<i>g</i>) KM curves of the validation set of 148 stage III-IV patients classified by the 6p+12p arraylet correlation, show a median survival time difference of 22 months, with the corresponding log-rank test <i>P</i>-value < 10⁻², and the univariate Cox proportional hazard ratio 1.9. This validates the survival analyses of the discovery set of 249 patients. (<i>h</i>) Survival analyses of the 148 patients classified by the 7p arraylet correlation. (<i>i</i>) The 148 patients classified by the Xq arraylet correlation.</p

Survival analyses of the discovery and validation sets of patients, as well as only the platinum-based chemotherapy patients in the discovery and validation sets, classified by the 6p+12p, 7p, and Xq tensor GSVD combined.

<p>(<i>a</i>) KM curves of the discovery set of 249 patients classified by combination of the 6p+12p, 7p, and Xq <i>x</i>-probelet coefficients, show median survival times of 86, 52, and 36 months for the groups A, B, and C, respectively, with the corresponding log-rank test <i>P</i>-value < 10⁻³. (<i>b</i>) KM survival analysis of only the 218, i.e., ∼ 88% platinum-based chemotherapy patients in the discovery set, classified by combination of the three tensor GSVDs, gives qualitatively the same and quantitatively similar results to those of the analyses of 100% of the patients. This means that the combination of the three tensor GSVDs predicts survival in the platinum-based chemotherapy patient population. (<i>c</i>) KM curves of the validation set of 148 stage III-IV patients classified by combination of the 6p+12p, 7p, and Xq arraylet correlation coefficients, show median survival times of 72, 57, and 33 months for the groups A, B, and C, respectively, with the corresponding log-rank test <i>P</i>-value < 10⁻³. This validates the survival analyses of the discovery set of 249 patients. (<i>d</i>) KM survival analysis of only the 140, i.e., ∼ 95% platinum-based chemotherapy patients in the validation set, classified by combination of the three tensor GSVDs.</p

Tensor generalized singular value decomposition (GSVD) of the patient- and platform-matched DNA copy-number profiles of the 6p+12p chromosome arms.

<p>For each chromosome arm or combination of two chromosome arms, the structure of the tumor and normal discovery datasets (₁ and ₂) is that of two third-order tensors with one-to-one mappings between the column dimensions but different row dimensions. The patients, platforms, probes, and tissue types, each represent a degree of freedom. Unfolded into a single matrix, some of the degrees of freedom are lost and much of the information in the datasets might also be lost. We define a tensor GSVD that simultaneously separates the paired datasets into weighted sums of paired subtensors, i.e., combinations or outer products of three patterns each: Either one tumor-specific pattern of copy-number variation across the tumor probes, i.e., a tumor arraylet (a column basis vector of <i>U</i>₁), or the corresponding normal-specific arraylet (a column basis vector of <i>U</i>₂), combined with one pattern of variation across the patients, i.e., an <i>x</i>-probelet (a row basis vector of </p><p></p><p></p><p><mi>V</mi><mi>x</mi><mi>T</mi></p><p></p><p></p>), and one pattern across the platforms, i.e., a <i>y</i>-probelet (a row basis vector of <p></p><p></p><p><mi>V</mi><mi>y</mi><mi>T</mi></p><p></p><p></p>), which are identical for both the tumor and normal datasets (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121396#pone.0121396.e003" target="_blank">Equation 1</a>). The tensor GSVD is depicted in a raster display, with relative copy-number gain (red), no change (black), and loss (green), explicitly showing the first through the 5th, and the 245th through the 249th 6p+12p <i>x</i>-probelets, both 6p+12p <i>y</i>-probelets, and the first through the 10th, and the 489th through the 498th 6p+12p tumor and normal arraylets. We prove that the significance of a subtensor in the tumor dataset relative to that of the corresponding subtensor in the normal dataset, i.e., the tensor GSVD angular distance, equals the row mode GSVD angular distance, i.e., the significance of the corresponding tumor arraylet in the tumor dataset relative to that of the normal arraylet in the normal dataset. The tensor GSVD angular distances for the 498 pairs of 6p+12p arraylets are depicted in a bar chart display, where the angular distance corresponding to the first pair of arraylets is ∼ <i>π</i>/4. For the 6p+12p combination of two chromosome arms, we find that the most significant subtensor in the tumor dataset (which corresponds to the coefficient of largest magnitude in ℛ₁) is a combination of (<i>i</i>) the first <i>y</i>-probelet, which is approximately invariant across the platforms, (<i>ii</i>) the first <i>x</i>-probelet, which classifies the discovery set of patients into two groups of high and low coefficients, of significantly and robustly different prognoses, and (<i>iii</i>) the first, most tumor-exclusive tumor arraylet, which classifies the validation set of patients into two groups of high and low correlations of significantly different prognoses consistent with the <i>x</i>-probelet’s classification of the discovery set.<p></p