Evidence map›Paper›PMID 31284461›Full record

ArticleJournal of personalized medicine2019

Adaptive, Iterative, Long-Term Personalized Therapy Management in a Case of Stage IV Refractory NSCLC.

Anantbhushan Ranade, Darshana Patil, Amit Bhatt, Rucha Dhasare, Vineet Datta, Rajan Datar, Dadasaheb Akolkar

Abstract readCase Reports
In one paragraph

Article in Journal of personalized medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anantbhushan RanadeAvinash Cancer Clinic, Tilak Road, Pantancha Gate, Sadashiv Peth, Pune 411030, Maharashtra, India.
Darshana PatilDatar Cancer Genetics Ltd., F-8 D-Road, Ambad, Nasik 422010, Maharashtra, India.
Amit BhattAvinash Cancer Clinic, Tilak Road, Pantancha Gate, Sadashiv Peth, Pune 411030, Maharashtra, India.
Rucha DhasareAvinash Cancer Clinic, Tilak Road, Pantancha Gate, Sadashiv Peth, Pune 411030, Maharashtra, India.
Vineet DattaDatar Cancer Genetics Ltd., F-8 D-Road, Ambad, Nasik 422010, Maharashtra, India.
Rajan DatarDatar Cancer Genetics Ltd., F-8 D-Road, Ambad, Nasik 422010, Maharashtra, India.
Dadasaheb AkolkarDatar Cancer Genetics Ltd., F-8 D-Road, Ambad, Nasik 422010, Maharashtra, India. dadasaheb.akolkar@datarpgx.com.ORCID 0000-0002-4434-488X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper we report long-term therapy management based on iterative de novo molecular and cellular analysis in a case of metastatic non-small cell lung cancer (NSCLC), with prior history of treated colorectal cancer. In the described case temporal tumor evolution, emergent therapy resistance and disease recurrences were addressed via the administration of personalized label- and organ-agnostic treatments based on de novo tumor profiling. This adaptive and iterative treatment strategy countered disease progression at each instance and led to the durable regression of primary as well as metastatic lesions. Concurrently, serial evaluation of mutations in cell-free circulating tumor DNA (ctDNA) via liquid biopsy (LBx) was performed to monitor disease status, ascertain treatment response, identify emergent drug resistance and detect recurrence at sub-radiological levels. The treatment management strategy described herein effectively addressed multiple, sequential clinical conundrums for which viable options were unavailable under the current Standard of Care (SoC).

Indexed as

non small cell lung cancer (NSCLC)personalized therapy managementprecision oncology

Identifiers

PMID31284461
PMCPMC6789881

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.