Publications by authors named "Hira Rizvi"

PURPOSEThe impact of the intratumoral microbiome on immune checkpoint inhibitor (ICI) efficacy in patients with non-small-cell lung cancer (NSCLC) is unknown. Preclinically, intratumoral Escherichia is associated with a proinflammatory tumor microenvironment and decreased metastases. We sought to determine whether intratumoral Escherichia is associated with outcome to ICI in patients with NSCLC.

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  • Immunotherapy using PD-(L)1 blockade is common for treating non-small cell lung cancer (NSCLC), but over 60% of initial responders develop acquired resistance.
  • Research indicates that this resistance is linked to differences in inflammation and interferon (IFN) signaling, with relapsed tumors showing varied expressions of IFNγ response genes.
  • Understanding the altered IFN response in these tumors may help develop new strategies to overcome resistance and improve treatment effectiveness.
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Background: Direct KRASG12C inhibitors are approved for patients with non-small cell lung cancers (NSCLC) in the second-line setting. The standard-of-care for initial treatment remains immune checkpoint inhibitors, commonly in combination with platinum-doublet chemotherapy (chemo-immunotherapy). Outcomes to chemo-immunotherapy in this subgroup have not been well described.

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  • A study compared the effectiveness of single-agent PD-(L)1 blockade therapy (IO) versus a combination of chemotherapy and PD-(L)1 blockade (Chemotherapy-IO) in patients with advanced lung adenocarcinomas (LUADs) who had PD-L1 expression of 1% or more.
  • The results showed that Chemotherapy-IO led to a higher objective response rate (44% vs 35%) and longer progression-free survival compared to IO alone, particularly in patients with higher PD-L1 expression.
  • However, only never-smokers with PD-L1 expression of 50% or more showed a significant survival benefit from the combination treatment, while patients with very high PD-L1 expression (
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Purpose: We sought to identify features of patients with advanced non-small cell lung cancer (NSCLC) who achieve long-term response (LTR) to immune checkpoint inhibitors (ICI), and how these might differ from features predictive of short-term response (STR).

Experimental Design: We performed a multicenter retrospective analysis of patients with advanced NSCLC treated with ICIs between 2011 and 2022. LTR and STR were defined as response ≥ 24 months and response < 12 months, respectively.

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Introduction: Although gene-level copy number alterations have been studied as a potential biomarker of immunotherapy efficacy in NSCLC, the impact of aneuploidy burden and chromosomal arm-level events on immune checkpoint inhibitor (ICI) efficacy in NSCLC is uncertain.

Methods: Patients who received programmed cell death protein 1 or programmed death-ligand 1 (PD-L1) inhibitor at two academic centers were included. Across all 22 chromosomes analyzed, an arm was considered altered if at least 70% of its territory was either gained or deleted.

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Purpose: We describe the clinical and genomic landscape of the non-small cell lung cancer (NSCLC) cohort of the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC).

Experimental Design: A total of 1,846 patients with NSCLC whose tumors were sequenced from 2014 to 2018 at four institutions participating in AACR GENIE were randomly chosen for curation using the PRISSMM data model. Progression-free survival (PFS) and overall survival (OS) were estimated for patients treated with standard therapies.

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Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including (1) favorable (for example, ATM altered) and unfavorable (for example, TERT amplified) genomic subgroups, (2) a prominent association between expression of inducible components of the immunoproteasome and response and (3) a dedifferentiated tumor-intrinsic subtype with enhanced response to checkpoint blockade.

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  • Paired single-cell RNA and T cell receptor sequencing (scRNA/TCR-seq) was used to analyze T cell dynamics in non-small cell lung cancer after immune checkpoint blockade, focusing on 187,650 T cells from various tissue regions.
  • The findings indicated that regions with active tumors had high levels of exhausted CD8 T cells, regulatory CD4 T cells (Tregs), and follicular helper T cells (TFH), showing changes in T cell populations based on their location relative to the tumor.
  • The study also tracked specific T cell clones over time, finding that tumor-specific T cells persist in the bloodstream for years following treatment, demonstrating a long-lasting immune response post-therapy.
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  • * Analysis of 440 lung cancer samples showed that CD39+ CD8 T cells were linked to features like exhaustion and tumor reactivity, but only weakly associated with other tumor characteristics like PD-L1 and mutation burden.
  • * Increased levels of CD39+ CD8 T cells due to immune checkpoint blockade (ICB) were linked to better outcomes in ICB therapy, with a specific gene signature predicting benefits from ICB but not from chemotherapy in non-small cell lung cancer trials.
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  • The RAS family of GTPases, often mutated in various cancers, was analyzed across 66,372 tumors to understand how RAS mutations affect other genes and overall cancer characteristics.
  • The study revealed that RAS mutations show different patterns of co-mutations with non-RAS genes, influenced by the type of cancer, patient demographics, and genetic factors, particularly noting distinctions in KRAS G12C-mutant lung cancer.
  • Findings suggest that understanding the genomic differences in RAS-mutant tumors can help develop targeted therapies and improve clinical outcomes, especially with immunotherapy strategies.
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  • - Accurate understanding of genetic ancestry is essential for addressing cancer disparities, and new methods using clinical sequencing panels allow for better integration of ancestry analysis in diagnostic settings.
  • - In a study of over 45,000 cancer patients, researchers found that ancestry influences the frequency of genetic mutations, revealing some known and novel associations, as well as differences in clinically actionable alterations.
  • - Despite similar rates of key genetic changes by ancestry group, a lower percentage of patients with African ancestry had actionable alterations compared to those with European ancestry, highlighting inequities in how precision oncology serves different populations.
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  • Immunotherapy is commonly used for advanced non-small cell lung cancer (NSCLC), but accurately predicting who will respond to it remains difficult.
  • The study analyzed data from 247 patients, combining medical imaging, histopathology, and genomic features to improve predictions about immunotherapy responses.
  • The proposed multimodal model significantly outperformed traditional single-method measures, showing a strong predictive capacity (AUC = 0.80) and highlighting the value of using multiple data types in patient assessments.
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Purpose: Clinical patterns and the associated optimal management of acquired resistance to PD-(L)1 blockade are poorly understood.

Experimental Design: All cases of metastatic lung cancer treated with PD-(L)1 blockade at Memorial Sloan Kettering were reviewed. In acquired resistance (complete/partial response per RECIST, followed by progression), clinical patterns were distinguished as oligo (OligoAR ≤ 3 lesions of disease progression) or systemic (sAR).

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Importance: Although tumor mutation burden (TMB) has been explored as a potential biomarker of immunotherapy efficacy in solid tumors, there still is a lack of consensus about the optimal TMB threshold that best discriminates improved outcomes of immune checkpoint inhibitor therapy among patients with non-small cell lung cancer (NSCLC).

Objectives: To determine the association between increasing TMB levels and immunotherapy efficacy across clinically relevant programmed death ligand-1 (PD-L1) levels in patients with NSCLC.

Design, Setting, And Participants: This multicenter cohort study included patients with advanced NSCLC treated with immunotherapy who received programmed cell death-1 (PD-1) or PD-L1 inhibition in the Dana-Farber Cancer Institute (DFCI), Memorial Sloan Kettering Cancer Center (MSKCC), and in the Stand Up To Cancer (SU2C)/Mark Foundation data sets.

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Background: The genetic factors that modulate risk for developing lung cancer have not been fully defined. Here, we sought to determine the prevalence and clinical significance of germline pathogenic/likely pathogenic variants (PV) in patients with advanced lung cancer.

Methods: We studied clinical and tumor characteristics of germline PV in 5,118 patients who underwent prospective genomic profiling using paired tumor-normal tissue samples in 468 cancer genes.

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Purpose: The American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative is a multi-institution effort to build a pan-cancer repository of genomic and clinical data curated from the electronic health record. For the research community to be confident that data extracted from electronic health record text are reliable, transparency of the approach used to ensure data quality is essential.

Materials And Methods: Four institutions participating in AACR's Project GENIE created an observational cohort of patients with cancer for whom tumor molecular profiling data, therapeutic exposures, and treatment outcomes are available and will be shared publicly with the research community.

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Introduction: Patients with thoracic malignancies are at increased risk for mortality from coronavirus disease 2019 (COVID-19), and a large number of intertwined prognostic variables have been identified so far.

Methods: Capitalizing data from the Thoracic Cancers International COVID-19 Collaboration (TERAVOLT) registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure, and a tree-based model to screen and optimize a broad panel of demographics and clinical COVID-19 and cancer characteristics.

Results: As of April 15, 2021, a total of 1491 consecutive eligible patients from 18 countries were included in the analysis.

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Introduction: STK11 and KEAP1 mutations (STK11 mutant [STK11] and KEAP1) are among the most often mutated genes in lung adenocarcinoma (LUAD). Although STK11 has been associated with resistance to programmed death-(ligand)1 (PD-[L]1) inhibition in KRAS LUAD, its impact on immunotherapy efficacy in KRAS wild-type (KRAS) LUAD is currently unknown. Whether KEAP1 differentially affects outcomes to PD-(L)1 inhibition in KRAS and KRAS LUAD is also unknown.

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Importance: Real-world data sets that combine clinical and genomic data may be subject to left truncation (when potential study participants are not included because they have already passed the milestone of interest at the time of study recruitment). The lapse between diagnosis and molecular testing can present analytic challenges and threaten the validity and interpretation of survival analyses.

Observations: Effects of ignoring left truncation when estimating overall survival are illustrated using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC), and a straightforward risk-set adjustment approach is described.

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Objectives: To investigate the inter- and intra-reader agreement of immune Response Evaluation Criteria in Solid Tumors (iRECIST) and Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) in patients with lung cancer treated with immunotherapy.

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Background: Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progress rapidly; for others with advanced sarcomatoid cancers, a poor prognosis can progress slowly. Therefore, we aimed to develop and validate a machine-learning tool-known as OncoCast-MPM-that could create a model for patient prognosis.

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Introduction: The optimal management for immune-related adverse events (irAEs) in patients who do not respond or become intolerant to steroids is unclear. Guidelines suggest additional immunosuppressants on the basis of case reports and expert opinion.

Methods: We evaluated patients with lung cancers at Memorial Sloan Kettering Cancer Center treated with immune checkpoint blockade from 2011 to 2020.

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Article Synopsis
  • The study investigates how genetic differences in immunity affect thyroid-related immune adverse events (irAEs) in patients with non-small cell lung cancer (NSCLC) undergoing immune checkpoint inhibitor (CPI) therapy at two medical centers.
  • Out of 744 patients, 13% experienced thyroid irAEs, which correlated with better progression-free survival, and a developed polygenic risk score (PRS) for hypothyroidism effectively predicted the likelihood of these irAEs in both clinical cohorts.
  • While thyroid irAEs were linked to improved treatment responses, the PRS for hypothyroidism did not identify who would benefit from CPI therapy, suggesting more research is needed into potential genetic links between various irAEs and
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The lack of knowledge about the relationship between tumor genotypes and therapeutic responses remains one of the most critical gaps in enabling the effective use of cancer therapies. Here, we couple a multiplexed and quantitative experimental platform with robust statistical methods to enable pharmacogenomic mapping of lung cancer treatment responses . The complex map of genotype-specific treatment responses uncovered that over 20% of possible interactions show significant resistance or sensitivity.

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