ALIGNMENT-FREE METHODS AND ITS APPLICATIONS

Comparing biological sequences remains one of the most vital activities in Bioinformatics. Comparing biological sequences would address the relatedness between species, and find similar structures that might lead to similar functions. Sequence alignment is the default method, and has been used in the domain for over four decades. It gained a lot of trust, but limitations and even failure has been reported, especially with the new generated genomes. These new generated genomes have bigger size, and to some extent suffer errors. Such errors come mainly as a result from the sequencing machine. These sequencing errors should be considered when submitting sequences to GenBank, for sequence comparison, it is often hard to address or even trace this problem. Alignment-based methods would fail with such errors, and even if biologists still trust them, reports showed failure with these methods. The poor results of alignment-based methods with erratic sequences, motivated researchers in the domain to look for alternatives. These alternative methods are alignment-free, and would overcome the shortcomings of alignment-based methods. The work of this thesis is based on alignment-free methods, and it conducts an in-depth study to evaluate these methods, and find the right domain’s application for them. The right domain for alignment-free methods could be by applying them to data that were subjected to manufactured errors, and test the methods provide better comparison results with data that has naturally severe errors. The two techniques used in this work are compression-based and motif-based (or k-mer based, or signal based). We also addressed the selection of the used motifs in the second technique, and how to progress the results by selecting specific motifs that would enhance the quality of results. In addition, we applied an alignment-free method to a different domain, which is gene prediction. We are using alignment-free in gene prediction to speed up the process of providing high quality results, and predict accurate stretches in the DNA sequence, which would be considered parts of genes

Expression of TIM3/VISTA checkpoints and the CD68 macrophage-associated marker correlates with anti-PD1/PDL1 resistance: implications of immunogram heterogeneity.

Although immunotherapies have achieved remarkable salutary effects among subgroups of advanced cancers, most patients do not respond. We comprehensively evaluated biomarkers associated with the "cancer-immunity cycle" in the pan-cancer setting in order to understand the immune landscape of metastatic malignancies as well as anti-PD-1/PD-L1 inhibitor resistance mechanisms. Interrogation of 51 markers of the cancer-immunity cycle was performed in 101 patients with diverse malignancies using a clinical-grade RNA sequencing assay. Overall, the immune phenotypes demonstrated overexpression of multiple checkpoints including VISTA (15.8% of 101 patients), PD-L2 (10.9%), TIM3 (9.9%), LAG3 (8.9%), PD-L1 (6.9%) and CTLA4 (3.0%). Additionally, aberrant expression of macrophage-associated markers (e.g. CD68 and CSF1R; 11-23%), metabolic immune escape markers (e.g. ADORA2A and IDO1; 9-16%) and T-cell priming markers (e.g. CD40, GITR, ICOS and OX40; 4-31%) were observed. Most tumors (87.1%, 88/101) expressed distinct immune portfolios, with a median of six theoretically actionable biomarkers (pharmacologically tractable by Food and Drug Administration approved agents [on- or off-label] or with agents in clinical development). Overexpression of TIM-3, VISTA and CD68 were significantly associated with shorter progression-free survival (PFS) after anti-PD-1/PD-L1-based therapies (among 39 treated patients) (all P < .01). In conclusion, cancer-immunity cycle biomarker evaluation was feasible in diverse solid tumors. High expression of alternative checkpoints TIM-3 and VISTA and of the macrophage-associated markers CD68 were associated with significantly worse PFS after anti-PD-1/PD-L1-based therapies. Most patients had distinct and complex immune expression profiles suggesting the need for customized combinations of immunotherapy

Expression of TIM3/VISTA checkpoints and the CD68 macrophage-associated marker correlates with anti-PD1/PDL1 resistance: implications of immunogram heterogeneity

Although immunotherapies have achieved remarkable salutary effects among subgroups of advanced cancers, most patients do not respond. We comprehensively evaluated biomarkers associated with the “cancer-immunity cycle” in the pan-cancer setting in order to understand the immune landscape of metastatic malignancies as well as anti-PD-1/PD-L1 inhibitor resistance mechanisms. Interrogation of 51 markers of the cancer-immunity cycle was performed in 101 patients with diverse malignancies using a clinical-grade RNA sequencing assay. Overall, the immune phenotypes demonstrated overexpression of multiple checkpoints including VISTA (15.8% of 101 patients), PD-L2 (10.9%), TIM3 (9.9%), LAG3 (8.9%), PD-L1 (6.9%) and CTLA4 (3.0%). Additionally, aberrant expression of macrophage-associated markers (e.g. CD68 and CSF1R; 11-23%), metabolic immune escape markers (e.g. ADORA2A and IDO1; 9-16%) and T-cell priming markers (e.g. CD40, GITR, ICOS and OX40; 4-31%) were observed. Most tumors (87.1%, 88/101) expressed distinct immune portfolios, with a median of six theoretically actionable biomarkers (pharmacologically tractable by Food and Drug Administration approved agents [on- or off-label] or with agents in clinical development). Overexpression of TIM-3, VISTA and CD68 were significantly associated with shorter progression-free survival (PFS) after anti-PD-1/PD-L1-based therapies (among 39 treated patients) (all P < .01). In conclusion, cancer-immunity cycle biomarker evaluation was feasible in diverse solid tumors. High expression of alternative checkpoints TIM-3 and VISTA and of the macrophage-associated markers CD68 were associated with significantly worse PFS after anti-PD-1/PD-L1-based therapies. Most patients had distinct and complex immune expression profiles suggesting the need for customized combinations of immunotherapy

Expression of TIM3/VISTA checkpoints and the CD68 macrophage-associated marker correlates with anti-PD1/PDL1 resistance: implications of immunogram heterogeneity.

Although immunotherapies have achieved remarkable salutary effects among subgroups of advanced cancers, most patients do not respond. We comprehensively evaluated biomarkers associated with the "cancer-immunity cycle" in the pan-cancer setting in order to understand the immune landscape of metastatic malignancies as well as anti-PD-1/PD-L1 inhibitor resistance mechanisms. Interrogation of 51 markers of the cancer-immunity cycle was performed in 101 patients with diverse malignancies using a clinical-grade RNA sequencing assay. Overall, the immune phenotypes demonstrated overexpression of multiple checkpoints including VISTA (15.8% of 101 patients), PD-L2 (10.9%), TIM3 (9.9%), LAG3 (8.9%), PD-L1 (6.9%) and CTLA4 (3.0%). Additionally, aberrant expression of macrophage-associated markers (e.g. CD68 and CSF1R; 11-23%), metabolic immune escape markers (e.g. ADORA2A and IDO1; 9-16%) and T-cell priming markers (e.g. CD40, GITR, ICOS and OX40; 4-31%) were observed. Most tumors (87.1%, 88/101) expressed distinct immune portfolios, with a median of six theoretically actionable biomarkers (pharmacologically tractable by Food and Drug Administration approved agents [on- or off-label] or with agents in clinical development). Overexpression of TIM-3, VISTA and CD68 were significantly associated with shorter progression-free survival (PFS) after anti-PD-1/PD-L1-based therapies (among 39 treated patients) (all P &lt;&nbsp;.01). In conclusion, cancer-immunity cycle biomarker evaluation was feasible in diverse solid tumors. High expression of alternative checkpoints TIM-3 and VISTA and of the macrophage-associated markers CD68 were associated with significantly worse PFS after anti-PD-1/PD-L1-based therapies. Most patients had distinct and complex immune expression profiles suggesting the need for customized combinations of immunotherapy

Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy.

Next-generation sequencing (NGS) can identify novel cancer targets. However, interpreting the molecular findings and accessing drugs/clinical trials is challenging. Furthermore, many tumors show resistance to monotherapies. To implement a precision strategy, we initiated a multidisciplinary (basic/translational/clinical investigators, bioinformaticians, geneticists, and physicians from multiple specialties) molecular tumor board (MTB), which included a project manager to facilitate obtaining clinical-grade biomarkers (blood/tissue NGS, specific immunohistochemistry/RNA expression including for immune-biomarkers, per physician discretion) and medication-acquisition specialists/clinical trial coordinators/navigators to assist with medication access. The MTB comprehensively reviewed patient characteristics to develop N-of-One treatments implemented by the treating physician's direction under the auspices of a master protocol. Overall, 265/429 therapy-evaluable patients (62%) were matched to ≥1 recommended drug. Eighty-six patients (20%) matched to all drugs recommended by MTB, including combinatorial approaches, while 38% received physician's choice regimen, generally with unmatched approach/low degree of matching. Our results show that patients who receive MTB-recommended regimens (versus physician choice) have significantly longer progression-free (PFS) and overall survival (OS), and are better matched to therapy. High (≥50%) versus low (&lt;50%) Matching Score therapy (roughly reflecting therapy matched to ≥50% versus &lt;50% of alterations) independently correlates with longer PFS (hazard ratio [HR], 0.63; 95% confidence interval [CI], 0.50-0.80; P &lt; 0.001) and OS (HR, 0.67; 95% CI, 0.50-0.90; P = 0.007) and higher stable disease ≥6 months/partial/complete remission rate (52.1% versus 30.4% P &lt; 0.001) (all multivariate). In conclusion, patients who receive MTB-based therapy are better matched to their genomic alterations, and the degree of matching is an independent predictor of improved oncologic outcomes including survival