Category: cancer

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Breakthrough advances in 2018 so far: flu, germs, and cancer

2018 medicine breakthrough review!

So far this year has seen some pretty important research breakthrough advances in several key areas of health and medicine.  I want to briefly describe some of what we’ve seen in just the first few months of 2018.

Flu

A pharmaceutical company in Japan has released phase 3 trial results showing that its drug, Xofluza, can effectively kill the virus in just 24 hours in infected humans.  And it can do this with just one single dose, compared to a 10-dose, three day regimen of Tamiflu. The drug works by inhibiting an endonuclease needed for replication of the virus.

Germs

It is common knowledge that antibiotics are over-prescribed and over-used.  This fact has led to the rise of MRSA and other resistant bacteria which threaten human health.  Although it is thought that bacteria could be a source of novel antibiotics since they are in constant chemical warfare with each other, most bacteria aren’t culture-friendly in the lab and so researchers haven’t been looking at them for leads.  Until now.

Malacidin drugs kill multi-drug resistant S. Aureus in tests on rats.

By adopting whole genome sequencing approaches to soil bacterial diversity, researchers were able to screen for gene clusters associated with calcium-binding motifs known for antibiotic activity.   The result was the discovery of a novel class of lipo-peptides, called malacidins A and B.  They showed potent activity against MRSA in skin infection models in rats.

The researchers estimate that 99% of bacterial natural-product antibiotic compounds remain unexplored at present.

Cancer

2017 and 2018 have seen some major advances with cancer treatment.   It seems that the field is moving away from the focus on small-molecule drugs towards harnessing the patient’s own immune system to attack cancer.  The CAR-T therapies for pediatric leukemia appear extremely promising.  These kinds of therapies are now in trials for a wide range of blood and solid tumors.

A great summary of the advances being made is available here from the Fred Hutchinson Cancer Research Center.   Here is how Dr. Gilliland, President of Fred Hutch, begins his review of the advances:

I’ve gone on record to say that by 2025, cancer researchers will have developed curative therapeutic approaches for most if not all cancers.

I took some flak for putting that stake in the ground. But we in the cancer research field are making incredible strides toward better and safer, potentially curative treatments for cancer, and I’m excited for what’s next. I believe that we must set a high bar, execute and implement — that there should be no excuses for not advancing the field at that pace.

This is a stunning statement on its own;  but made even more so because it is usually the scientists in the day-to-day trenches of research who are themselves the most pessimistic about the possibility of rapid advances.

Additionally, an important paper came out recently proposing a novel paradigm for understanding and modeling cancer incidence with age.  For a long time the dominant model has been the “two-hit” hypothesis which predicts that clinically-observable cancers arise when a cell acquires sufficient mutations in tumor-suppressor genes to become a tumor.

This paper challenges that notion and shows that a model of thymic function decline (the thymus produces T-cells) over time better describes the incidence of cancers with age.   This model better fits the data and leads to the conclusion that cancers are continually arising in our bodies, but it is our properly functioning immune system that roots them out and prevents clinical disease from emerging.  This model also helps explain why novel cancer immunotherapies are so potent and why focus has shifted to supporting and activating T-cells.

Declining T cell production leads to increasing disease incidence with age.

 

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Is CB-5083 a promising new weapon against multiple myeloma?

Why care about p97?

In my postdoc work, I participated in a large team effort at designing a small molecule inhibitor of the p97 AAA-ATPase.

A crystal structure of the p97 ATPase.  The D2 domain is shown in dark blue.

The funding for this project came from the National Cancer Institute (NCI) and was premised on the idea that inhibiting p97 in certain types of cancer cells that depend heavily on the endoplasmic-reticulum associated degradation pathway (ERAD) would have the effect of triggering the unfolded-protein response and apoptosis pathways within the rapidly growing tumor cell populations.   This is because p97 is a critical regulator and component of ERAD, and when it is inhibited, the cell experiences unbalanced protein homeostasis and unfolded protein stress.

Drug design is an extremely challenging problem, and even with a large group of researchers it took us several years to find a compound that showed promising inhibition against p97.   Our results were published in ACS Med Chem Letters in 2016.   The compound we discovered, indole amide 3, has high solubility, permeability, and stability.  It binds an allosteric site on the D2 domain  with sub-micromolar affinity.   Unfortunately, it just didn’t have enough binding affinity to be active in vivo.

A different approach yields new promise

At around the same time we were developing our allosteric inhibitor series, another group was developing an ATP competitive D2 domain inhibitor of p97, called CB-5083.  In contrast to our compound, this one binds directly to the D2 ATP enzyme site with nanomolar affinity.

CB-5083.

The compound also demonstrated potent and specific p97 inhibition activity in mouse xenograft models of tumors.

An advance in myeloma cancer therapy

A more recent paper (Nov 2017) shows activity for CB-5083 against multiple myeloma (MM) cell lines and in vivo MM models.  From the abstract:

CB-5083 decreases viability in multiple myeloma cell lines and patient-derived multiple myeloma cells, including those with background proteasome inhibitor (PI) resistance. CB-5083 has a unique mechanism of action that combines well with PIs, which is likely owing to the p97-dependent retro-translocation of the transcription factor, Nrf1, which transcribes proteasome subunit genes following exposure to a PI. In vivo studies using clinically relevant multiple myeloma models demonstrate that single-agent CB-5083 inhibits tumor growth and combines well with multiple myeloma standard-of-care agents.

Standard of care agents, like bortezomib, are proteasome inhibitors (PI).  Using a PI results in broad inhibition of the proteasome system across many cell types, not just tumor cells, and thus a high likelihood of side effects.  p97 is upstream of the proteasome and targeting it is more narrow in scope, because MM cells rely so heavily on the protein homeostasis activities of the ERAD pathway.

Hope for Phase 1 success

CB-5083 was also found to enhance the activity of bortezomib both in vitro and in vivo and also was active in bortezomib-resistance models of MM.  This paves the way for a potential combination therapy or another line of therapy if resistance develops as a result of earlier treatment with PIs.   Clinical trials are now ongoing in Phase 1 for patients who have exhausted other medications.  Hopefully CB-5083 makes it to the market soon, if trials prove it to be safe and efficacious, so that oncologists and patients have another weapon in the fight against MM.

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Genomic landscape of metastatic cancer

Integrative genomics sheds new light on metastatic cancer

A new study from the University of Michigan Comprehensive Cancer Center has just been released that represents an in-depth look at the genomics of metastatic cancer, as opposed to primary tumors.   This work involved DNA- and RNA-Seq of solid metastatic tumors of 500 adult patients, as well as matched normal tissue sequencing for detection of somatic vs. germline variants.

tl;dr:

A good overview of the study at the level of scientific layperson can be found in this press release.  It summarizes the key findings (many of which are striking and novel):

  • A significant increase in mutational burden of metastatic tumors vs. primary tumors.
  • A long-tailed distribution of mutational frequencies (i.e., few genes were mutated at a high rate, yet many genes were mutated).
  • About twelve percent of patients harbored germline variants that are suspected to predispose to cancer and metastasis, and 75% of those variants were in DNA repair pathways.
  • Across the cohort, 37% of patient tumors harbored gene fusions that either drove metastasis or suppressed the cells anti-tumor functions.
  • RNA-Seq showed that metastatic tumors are significantly de-differentiated, and fall into two classes:  proliferative and EMT-like (endothelial-to-mesenchymal transition).

 A brief look at the data

This study provides a high-level view onto the mutational burden of metastatic cancer vis-a-vis primary tumors.  Figure 1C from the paper shows the comparison of mutation rates in different tumor types in the TCGA (The Cancer Genome Atlas) primary tumors and the MET500 (metastatic cohort).

Mutational burden in metastatic cancer compared to primary tumors.

 

Here we can see that in most cases (colored bars), metastatic cancers had statistically significant increases in mutational rates.   The figure shows that tumors with low mutational rates “sped up” a lot as compared with those primary tumor types that already had high rates.

Supplemental Figure 1d (below) shows how often key tumor suppressor and oncogenes are altered in metastatic cancer vs. primary tumors.  TP53 is found to be altered more frequently in metastatic thyroid, colon, lung, prostate, breast, and bladder cancers.   PTEN is mutated more in prostate tumors.  GNAS and PIK3CA are mutated more in thymoma, although this finding doesn’t reach significance in this case.  KRAS is altered more in colon and esophagus cancers, but again, these findings don’t reach significance after multiple correction.

Comparison of genetic alteration frequencies in metastatic and primary tumors.

 

One other figure I’d like to highlight briefly is Figure 3C from the paper, shown below:

Molecular structure of novel, potentially activating gene fusions in the metastatic tumors.

I wanted to mention this figure to illustrate the terrifying complexity of cancer.   Knowing which oncogenes are mutated, in which positions, and the effects of those mutations on gene expression networks is not enough to understand tumor evolution and metastasis.  There are also new genes being created that do totally new things, and these are unique on a per tumor basis.   None of the above structures have ever been observed before, and yet they were all seen from a survey of just 500 cancers.   In fact, ~40% of the tumors in the study cohort harbored at least one fusion suspected to be pathogenic.

There is much more to this work, but I will leave it to interested readers to go read the entire study.   I think this work is obviously tremendously important and novel, and represents the future of personalized medicine.  That is, a patient undergoing treatment for cancer will have their tumor or tumors biopsied and sequenced cumulatively over time to understand how the disease has evolved and is evolving, and to ascertain what weaknesses can be exploited for successful treatment.

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My favorite talks from GLBIO2017 in Chicago

GLBIO2017

I just got back from Great Lakes Bio 2017 (GLBIO2017) at the University of Illinois-Chicago (UIC) campus.  It was a great meeting and I really enjoyed the quality of the research presented as well as the atmosphere of the campus and neighborhood.

I was very surprised by just how nice the Chicago “West Loop” neighborhood near Randolph Street and down towards Greektown really is.  I had some great meals, including a memorable Italian dinner at Formentos.

But the purpose of this post is to briefly describe a few of my favorite talks from the meeting.  So here goes, in no particular order:

Kevin White, Tempus Labs:

I was really impressed with Kevin White’s GLBIO2017 talk and demo of his company’s technology (despite the ongoing technical A/V issues!)  Tempus labs is a clinical sequencing company but also an informatics company focused on cancer treatment that seeks to pull together all of the disparate pieces of patient data that float around in EHR databases and are oftentimes not connected in meaningful ways.

The company sequences patient samples (whole exome and whole genome) and then also hoovers up reams of patient EHR data using Optical Character Recognition (OCR), Natural Language Processing (NLP), and human expert curation to turn the free-form flat text of medical records from different clinics and systems into a form of “tidy data” that can be accessed from an internal database.

Then, clinical and genomic data are combined for each patient in a deep-learning system that looks at treatments and outcomes for other similar patients and presents the clinician with charts that show how patients in similar circumstances fared with varying treatments, given certain facts of genotype and tumor progression, etc…  The system is pitched as “decision support” rather than artificial “decision making.”  That is, a human doctor is still the primary decider of treatment for each patient, but the Tempus deep learning system will provide expert support and suggest probabilities for success at each critical care decision point.

The system also learns and identifies ongoing clinical trials, and will present relevant trials to the clinician so that patients can be informed of possibly beneficial trials that they can join.

Murat Eren,  merenlab.org

Murat Eren’s talk on tracking microbial colonization in fecal microbiome transplantation (i.e., “poop pills”) was excellent and very exciting.  Although the “n” was small (just 4 donors and 2 recipients) he showed some very interesting results from transferring fecal microbiota (FM) from healthy individuals to those with an inflammatory bowel disease.

Among the interesting results are the fact that he was able to assemble 97 metagenomes in the 4 donor samples.  Following the recipients at 4 and 8-weeks post FM transplant showed that the microbial genomes could be classed into those that transfer and colonize permissively (both recipients), those that colonize one or the other recipient, and those that fail to colonize both.  Taxa alone did not explain why some microbes colonized easily, while other failed to colonize.

He also showed that 8 weeks post FM transplant, the unhealthly recipients had improved symptoms but also showed that in a PCA analysis of the composition of the recipient gut and the healthy human gut from 151 human microbiome project (HMP) samples, the recipients moved into the “healthy” HMP cluster from being extreme outliers on day 0.

He also investigated differential gene function enrichment between the permissive colonizers and the microbes that never colonized recipient’s guts and found that sporulation genes may be a negative factor driving the failure (or success) of transplantation.   He proposed that the recent and notable failure of the Seres microbiome drug in clinical trials may be owing to the fact that the company killed the live cultures in favor of more stable spore-forming strains when formulating the drug.  His work would suggest that these strains are less successful at colonizing new hosts.

Bo Zhang, 3D genome browser

With the ever-increasing volume of genomic and regulatory data and the complexity of that data, there is a need for accessible interfaces to it.   Bo Zhang’s group at Penn State has worked to make a new type of genome browser available that focuses on the 3D structure of the genome, pulling together disparate datatypes including chromatin interaction data, ChIP-Seq, RNA-Seq, etc…  You can also browse a complete view of the regulatory landscape and 3D architecture of any region of the genome.  You can also check the expression of any queried gene across hundreds of tissue/cell types measured by the ENCODE consortium.  On the virtual 4C page, they provide multiple methods to link distal cis-regulatory elements with their potential target genes, including virtual 4C, ChIA-PET and cross-cell-type correlation of proximal and distal DHSs.

The 3D Genome Browser flow chart.

 

All in all, GLBIO2017 was a very enjoyable and informative meeting where I met a lot of great colleagues and learned much.  I am looking forward to next year!

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Unix one-liner to convert VCF to Oncotator format

Here is a handy unix one-liner to process mutect2 output VCF files into the 5 column, tab-separated format required by Oncotator for input (Oncotator is a web-based application that annotates human genomic point mutations and indels with transcripts and consequences). The output of Oncotator is a MAF-formatted file that is compatible with MutSigCV.

#!/bin/bash
FILES='*.vcf.gz'
for file in $FILES
do
zcat $file | grep -v "GL000*" | grep -v "FILTER" | grep "PASS" | cut -d$'\t' -f 1-5 | awk '$3=$2' | awk '$1="chr"$1' > $file.tsv
done

Breaking this down we have:

“zcat $file” :  read to stdout each line of a gzipped file

“grep -v “GL000*” :  exclude any variant that doesn’t map to a  named chromosome

“grep -v “FILTER” : exclude filter header lines

“grep “PASS””:  include all lines that pass mutect2 filters

“cut -d$’\t’ -f 1-5”  : cut on tabs and keep fields one through five

“awk ‘$3=$2’ :  set column 3 equal to column 2, i.e., start and end position are equal

“awk $1=’chr’$1″” : set column one equal to ‘chr’ plus column one (make 1 = chr1)